EAI Endorsed Transactions on Pervasive Health and Technology

Quantitation of Body Movement in a Motor Physical Therapy for Parkinson’s Disease

2023-09-25T09:09:09+00:00

The present work shows the quantitation of body movement in a motor physical therapy for Parkinson’s Disease (PD). In recent years, many activities of therapy were carried out remotely using common RGB cameras to capture the body movements. We analyze the body movements of 8 subjects with clinical diagnosis of PD, and compare them with a control group of 11 healthy volunteers, processing their respective RGB video recordings with a software that identifies 17 specific body keypoints while subjects perform two motor rehabilitation therapies (cervical and lumbar spine). All videos were analyzed by OpenPose algorithm and angles from keypoints detected were computed to infer the rotation, rate and amplitude of movement of head, shoulder, back and pelvis. The results show that OpenPose algorithm could be used in a home environment specially in follow-up and management of the motor rehabilitation therapy for Parkinson’s disease.

An improved ANN-based sequential global-local approximation for small medical data analysis

2023-09-26T09:21:05+00:00

INTRODUCTION: The task of approximation of complex nonlinear dependencies, especially in the case of short datasets, is important in various applied fields of medicine. Global approximation methods describe the generalized behavior of the model, while local methods explain the behavior of the model at specific data points. Global-local approximation combines both approaches, which makes such methods a powerful tool for processing short sets of medical data that can have both broad trends and local variations.

OBJECTIVES: This paper aims to improve the method of sequential obtaining global and local components of the response surface to increase the accuracy of prediction in the case of short sets of medical data.

METHODS: In this paper, the authors developed a new method that combined two ANNs: a non-iterative SGTM neural-like structure for obtaining the global component and GRNN as a powerful tool of local approximation in the case of short datasets.

RESULTS: The authors have improved the method of global-local approximation due to the use of a General Regression Neural Network instead of RBF ANN for obtaining the local component, which ensured an increase in the accuracy of the body fat prediction task. The authors optimized the operation of the method and investigated the efficiency of the sequential obtaining global and local components of the response surface in comparison with the efficiency using a number of existing methods.

CONCLUSION: The conducted experimental studies for solving the body fat prediction task showed the high efficiency of using the improved method in comparison with a number of existing methods, including ensemble methods.

Using Games as an Effective Intervention for Supporting Families Living with Dementia

2023-09-26T13:38:54+00:00

This paper explores the role of games in supporting dementia family caregivers during the pre- and early stages of the disease. It provides a comprehensive review of existing studies that focus on support mechanisms for both dementia patients and their caregivers, with a specific emphasis on games designed for this purpose.

This paper outlines a research study conducted in two experiments. The first experiment involved conducting separate focus groups to explore how technology can assist older adults during the COVID-19 pandemic and its aftermath. Group A consisted of 9 participants from the UK, while Group B comprised 8 participants from Taiwan. The aim was to gather insights and perspectives from different cultural contexts. The second experiment of the study involved testing games with dementia family caregivers to assess their effectiveness and identify areas for refinement and improvement. A total of 20 participants took part in this experiment. By conducting focus groups and game testing with participants from different regions, this research aimed to gather diverse perspectives and insights, enhancing the validity and applicability of the findings.

The findings of this project extend beyond the scope of dementia care and have implications for addressing various long-term health conditions. Games platforms have the potential to serve as effective tools for supporting communities that provide care for individuals with dementia. They offer opportunities for promoting self-understanding, accessing relevant resources, and facilitating informed decision-making within the context of health journeys.

DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images

2023-10-03T12:55:30+00:00

INTRODUCTION: Cervical cancer is a deadly malignancy in the cervix, affecting billions of women annually.

OBJECTIVES: To develop deep learning-based system for effective cervical cancer detection by combining colposcopy and cytology screening.

METHODS: It employs DeepColpo for colposcopy and DeepCyto+ for cytology images. The models are trained on multiple datasets, including the self-collected cervical cancer dataset named Malhari, IARC Visual Inspection with Acetic Acid (VIA) Image Bank, IARC Colposcopy Image Bank, and Liquid-based Cytology Pap smear dataset. The ensemble model combines DeepColpo and DeepCyto+, using machine learning algorithms.

RESULTS: The ensemble model achieves perfect recall, accuracy, F1 score, and precision on colposcopy and cytology images from the same patients.

CONCLUSION: By combining modalities for cervical cancer screening and conducting tests on colposcopy and cytology images from the same patients, the novel approach achieved flawless results.

Real Time Lung Cancer Classification with YOLOv5

2023-09-20T13:00:16+00:00

Cancer must be appropriately categorized for effective diagnosis and treatment. Deep learning algorithms have shown tremendous promise in recent years for automating cancer classification. We used the deep learning system YOLOv5 to classify the four types of lung cancer in this study: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell carcinoma. We trained the YOLOv5 model using a publicly available database of lung cancer pictures. The dataset was divided into four categories: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell cancer. In addition, we compared YOLOv5's performance to older models such as SVM, RF, ANN, and CNN. The comparison found that YOLOv5 outperformed all these models, indicating its potential for the development of more accurate and efficient autonomous cancer classification systems. Conclusions from the research have important implications for cancer identification and therapy. Automatic cancer classification systems have the potential to increase the accuracy and efficacy of cancer detection, perhaps leading to better patient outcomes. The accuracy and speed of these systems can be enhanced by using deep learning techniques like YOLOv5, making them more effective for clinical applications. Our study's findings demonstrated high accuracy for every class, with a total accuracy of 97.77%. With the aid of accuracy, train loss, and test loss graphs, we assessed the model's performance. The graphs demonstrated how the model was able to gain knowledge from the data and increase its accuracy as it was being trained. The study's findings were also compiled in a table that gave a thorough assessment of each class's accuracy.

Disease Prediction Using a Modified Multi-Layer Perceptron Algorithm in Diabetes

2023-09-20T14:02:43+00:00

This paper presents an adaptation of the Multi-Layer Perceptron (MLP) algorithm for use in predicting diabetes risk. The aim is to enhance the accuracy and generalizability of the model by incorporating preprocessing techniques, dimensionality reduction using Principal Component Analysis (PCA), and improvements in optimization and regularization. Several factors, including glucose level, pregnancy, blood pressure, and body mass index, are taken into account when analyzing the PIMA Indian Diabetes dataset. Modern optimization methods, dropout regularization, and an adaptive learning rate are incorporated into the modified MLP model to fine-tune the model's weights and boost its predictive abilities. The effectiveness of the modified MLP algorithm is evaluated by comparing its performance with baseline machine learning methods and the original MLP algorithm in terms of accuracy, sensitivity, and specificity. The results of this study can improve the quality of healthcare provided to people at risk for developing diabetes and thus contribute to the development of better prediction models for the disease.

Glaucoma Classification using Light Vision Transformer

2023-09-21T08:28:17+00:00

INTRODUCTION: Nowadays one of the primary causes of permanent blindness is glaucoma. Due to the trade-offs, it makes in terms of portability, size, and cost, fundus imaging is the most widely used glaucoma screening technique.

OBJECTIVES:To boost accuracy,focusing on less execution time, and less resources consumption, we have proposed a vision transformer-based model with data pre-processing techniques which fix classification problems.

METHODS: Convolution is a “local” technique used by CNNs that is restricted to a limited area around an image. Self-attention, used by Vision Transformers, is a “global” action since it gathers data from the whole image. This makes it possible for the ViT to successfully collect far-off semantic relevance in an image. Several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad, were studied in this paper. We have trained and tested the Vision Transformer model on the IEEE Fundus image dataset having 1750 Healthy and Glaucoma images. Additionally, the dataset was preprocessed using image resizing, auto-rotation, and auto-adjust contrast by adaptive equalization.

RESULTS: Results also show that the Nadam Optimizer increased accuracy up to 97% in adaptive equalized preprocessing dataset followed by auto rotate and image resizing operations.

CONCLUSION: The experimental findings shows that transformer based classification spurred a revolution in computer vision with reduced time in training and classification.

Use of ICTs to assess the Risk of Diabetes in Educational Personnel: A Case Study

2023-09-21T09:41:45+00:00

Introduction: Type 2 diabetes mellitus today is one of the diseases that is currently seen at high levels and that increasingly the risk of suffering from it is increasing.

Objective: to determine The use of ICTs to assess the risk of diabetes in teachers of an educational institution in North Lima.

Methods: a quantitative, descriptive-transversal study, with a total population of 140 who answered a questionnaire of sociodemographic data and the diabetes mellitus risk test.

Results: 20.6% (n=21) have a minimal risk of diabetes mellitus, 24.5% (n=25) slightly elevated risks, 31.4% (n=32) moderate risk, 14.7% (n=15) elevated risk and 8.8% (n=9) extremely elevated risk.

Conclusions: the lifestyle should be modified to a more affordable one, which allows the adaptability of healthier eating behaviors that allow good health.

Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms

2023-09-21T10:02:39+00:00

Parkinson's Disease (PD) is a persistent neurological condition that has a global impact on a significant number of individuals. The timely detection of PD is imperative for the efficacious treatment and control of the condition. Machine learning (ML) methods have demonstrated significant potential in forecasting Parkinson's disease (PD) based on diverse data sources in recent times. The present research paper outlines a study that employs machine learning [ML]techniques to predict Parkinson's disease. A dataset comprising clinical and demographic characteristics of both patients diagnosed with PD and healthy individuals was taken from Kaggle. The aforementioned dataset was utilized to train and assess multiple machine learning models. The experimental findings indicate that the CatBoost model exhibited superior performance compared to the other models, achieving an accuracy rate of 95.1% and a root mean squared error of of 0.34.In summary, our research showcases the capabilities of machine learning methodologies in forecasting Parkinson's disease and offers valuable insights into the crucial predictors for PD prognosis. The results of our study could potentially contribute to the advancement of diagnostic methods for the timely identification of PD, with increased precision and efficacy.

Satisfaction with Life and its Relationship with Mental Health in University Professors

2023-09-21T10:24:15+00:00

Introduction: The satisfaction with the life in university professors will have an important value for the personal evaluation of themselves on the quality of their own experiences and in addition to a well-being of personal feeling.

Aim: to determine Satisfaction with life and its relationship with mental health in professors of a university in North Lima.

Methods: a quantitative, descriptive and cross-sectional study, with a population of 270 older adults, who answered a questionnaire of sociodemographic aspects and the scale of satisfaction with life.

Results: 54 (20%) of the older adults were dissatisfied with life, 32 (11.9%) slightly dissatisfied, 39 (14.4%) are neutral with respect to life satisfaction, 84 (31.1%) are satisfied, and 50 (18.5%) are very satisfied.

Conclusions: educational talks should be held for the elderly, where the experience of positive emotions during that stage of life continues to be promoted and allows them to have a better satisfaction with their lives.

Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model

2023-09-25T08:56:18+00:00

Cancer is a disease involving unusual cell growth likely to spread to other parts of the body. According to WHO 2020 report, colorectal malignancy is the globally accepted second leading cause of cancer related deaths. Colorectal malignancy arises when malignant cells often called polyp, grow inside the tissues of the colon or rectum of the large intestine. Colonoscopy, CT scan, Histopathological analysis are some manual approaches of malignancy detection that are time consuming and lead to diagnostic errors. Supervised CNN data model requires a large number of labeled training samples to learn parameters from images. In this study we propose an expert system that can detect the colorectal malignancy and identify the exact polyp area from complex images. In this approach an unsupervised Deep Belief Network (DBN) is applied for effective feature extraction and classification of images. The classified image output of DBN is utilized by Polyp Detector. Residual network and feature extractor components of Polyp Detector helps polyp inspector in pixel wise learning. Two stage polyp network (PLPNet) is a R-CNN architecture with two stage advantage. The first stage is the extension of R-CNN to detect the polyp lesion area through a location box also called Polyp Inspector. Second Stage performs polyp segmentation. Polyp Inspector transfers the learned semantics to the polyp segmentation stage. It helps to enhance the ability to detect polyp with improved accuracy and guide the learning process. Skip schemes enrich the feature scale. Publicly available CVC-Clinical DB and CVC Colon DB datasets are used for experiment purposes to achieve a better prediction capability for clinical practices.

Early Alzheimer’s Disease Detection Using Deep Learning

2023-09-26T08:45:53+00:00

The early detection of Alzheimer's disease, a neurodegenerative ailment that affects both cognitive and social functioning, can be accomplished using deep learning technology. Deep learning is more accurate and efficient than human diagnosis in detecting functional connectivity and changes in the brain networks of people with MCI. Early detection of Mild Cognitive Impairment (MCI) can reduce the disease's development. However, achieving high accuracy levels is difficult due to the dearth of reliable biomarkers. The dataset was picked up from the Kaggle database. It contains magnetic resonance images of the brain, each image being unique and in different stages of the disease for classification purpose for our project, as it was most suitable for our project’s needs. We developed a deep learning model using learning AZ net, Dense net, Resnet, Efficient Net and Inception Net with a maximum accuracy of 99.96% for classifying Alzheimer's disease stages and early detection using transfer learning and other approaches.

CURA: Real Time Artificial Intelligence and IoT based Fall Detection Systems for patients suffering from Dementia

2023-09-25T11:19:34+00:00

According to the rising concern of the effects on the families due to dementia suffering patients, we aim to provide caretakers a work-life balance in which monitoring can be done with much more ease and efficiency in real time. This device can also be used in old age homes as well as hospitals which reduces the workload of the caretakers and helps them to easily monitor the patients. We aim to contribute for the betterment of the society and provide a virtual assistance for the patients suffering from dementia. The number of elderly people living alone has been increasing all over the world. If dementia has been detected at an early stage, the progress of disease can be slowed. The patients suffering from dementia are prone to falling quite frequently so as to detect that and to alert their caretakers to take necessary actions. In this study, we proposed a system in which we detect the real time state of the elderly people living alone by using the Machine Learning and IoT (Internet of Things) technology.We installed sensors inside a finger strap which is attached to the person. These sensors can detect the motions of the patient and predict their real time state to have a 24 by 7 support to provide assistance to the patients.

A Big Data Survey on Lower Extremity Injuries and Prevention of Athletic Students in General Colleges and Universities

2023-09-26T09:52:43+00:00

INTRODUCTION: Athletics is trendy; many events rely heavily on lower body coordination. With the development of track and field, lower extremity injuries in track and field also occur frequently. In general colleges and universities, lower limb injuries in track and field not only affect students' physical and mental health but also affect students' daily life and Training.

OBJECTIVES: This paper examines the causes of lower limb injuries in students and suggests measures and recommendations for preventing lower limb injuries to increase the importance of lower limb injuries and reduce the rate of lower limb injuries in students.

METHODS: Combined with big data, the linear regression model was used, along with the literature method, questionnaire survey method and logical analysis method, to investigate the lower limb injuries of track and field students in general colleges and universities and analyze the survey results.

RESULTS: The following points were summarized: the lower limb injury rate of track and field students was as high as 79.03%, mainly focusing on ankles, followed by knees and joints; joint sprains dominated lower limb injuries, and the degree of injuries mainly was mild to moderate; the main factors affecting lower limb injuries included preparatory activities, technical movements, physical fitness and self-protection awareness; and the students did not have enough knowledge of and paid enough attention to the prevention of injuries.

CONCLUSION: The research in this paper can provide some references for more track and field students to help them have better careers.

A CDIO Education Model for Hospitality Management in Context of Artificial Intelligence and Informatization

2023-09-26T10:00:05+00:00

INTRODUCTION: Currently, demand for travel in China remains strong, and the tourism industry continues to grow. In the post-epidemic era, China's tourism industry is recovering and prospering, which also means that the tourism industry needs talent. As the demand for talent increases, basic hotel management training is gradually becoming the most significant factor affecting the development of applied professionals.

OBJECTIVES: To enhance the development of online higher education based on the Internet in China and promote the informatization of tourism management and hotel management teaching; to solve the current problems of deviation between theory and practice in hotel management majors and the inability of hotel management students to engage in related professions; and to promote the fusion of the CDIO education model and the traditional education model, and to fundamentally improve the quality of teaching in hotel management majors.

METHODS: In the study, firstly, literature research and theoretical research are used to conduct a detailed survey of CDIO education mode; then, the academic research results of CDIO are used to compare with the traditional hotel management teaching mode to summarize the problems existing in the conventional hotel management professional education; finally, information technology is measured through the empirical method to enhance the teaching and learning of hotel management.

RESULTS: There is currently a deviation between the theory and practice of online teaching in the hotel management program; the CDIO education model can better enhance hotel management teaching; and the overall understanding of on-campus practice-based learning, off-campus practice-based learning, and self-study skills of hotel management students is higher than average.

CONCLUSION: The concept and content of practice learning, the idea and standards of CDIO, the practice learning model of the Bachelor's degree in hospitality management, and the theoretical direction and feasibility of constructing a practice learning model for the Bachelor's degree in hospitality management based on CDIO theory were explored. The importance of referencing values and leadership in conjunction with progressive learning, curriculum renewal, and course design was found.

Optimized Deep Learning Model for Disease Prediction in Potato Leaves

2023-09-27T13:05:18+00:00

Food crops are important for nations and human survival. Potatoes are one of the most widely used foods globally. But there are several diseases hampering potato growth and production as well. Traditional methods for diagnosing disease in potato leaves are based on human observations and laboratory tests which is a cumbersome and time-consuming task. The new age technologies such as artificial intelligence and deep learning can play a vital role in disease detection. This research proposed an optimized deep learning model to predict potato leaf diseases. The model is trained on a collection of potato leaf image datasets. The model is based on a deep convolutional neural network architecture which includes data augmentation, transfer learning, and hyper-parameter tweaking used to optimize the proposed model. Results indicate that the optimized deep convolutional neural network model has produced 99.22% prediction accuracy on Potato Disease Leaf Dataset.

Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images

2023-09-29T12:19:59+00:00

The precise identification of FLAIR abnormalities in brain MR images is essential for diagnosing and managing lower-grade gliomas, segmentation continues to be a difficult task. In this research, we develop an exhaustive strategy that integrates advanced deep learning models such as DeepLabv3, U-Net, DenseNet121-Unet, ResNet50, Attention U-Net and EfficientNet to effectively segment FLAIR abnormalities in a dataset comprising 110 lower-grade glioma patients. The cancer Imaging achieve (TCIA), includes genomic cluster data and patient-specific details. Our methodology tackles the multi-class data imbalanced by employing a customized loss function, which merges Categorical Cross Entropy (CCE) WCE and WMDL functions are used to calculate loss, allowing the network to accurately segment smaller tumor regions. By performing dense network training on 3D picture patches, the suggested technique improves detection of border region artifacts and efficiently manages storage and system limited resources. We evaluate our strategy’s effectiveness on the presented dataset, emphasizing its potential for assisting correct diagnosis and individualized treatment strategies for patients with lower-grade gliomas.

Skin Disease Classification Using CNN Algorithms

2023-10-02T12:48:18+00:00

INTRODUCTION: Dermatological disorders, particularly human skin diseases, have become more common in recent decades. Environmental factors, socioeconomic problems, a lack of a balanced diet, and other variables have all contributed to an increase in skin diseases in recent years. Skin diseases can cause psychological suffering in addition to physical injury, especially in people with scarred or disfigured faces.

OBJECTIVES: The use of artificial intelligence or computer-based technologies in the detection of face skin disorders has advanced dramatically over time. Even for highly experienced doctors and dermatologists, identifying skin disorders can be tricky since many skin diseases have a visual affinity with the surrounding skin and lesions.

METHODS: Today, the majority of skincare specialists rely on time-consuming, traditional methods to identify disorders. Even though several research have demonstrated promising results on the picture classification job, few studies compare well-known deep learning models with various metrics for categorizing human skin disorders.

RESULTS: This study examines and contrasts various skin illnesses in terms of cosmetics and common skin concerns. Our dataset includes over 25000 of the eight most common skin disorders. Convolutional neural networks have shown imaging performance that is comparable to or greater than that of humans. We used 11 different network algorithms to identify the illnesses in the sample and compared the results.

CONCLUSION: To adjust the format of incoming photographs, we do certain image pre-processing and image scaling for each model. ResNet152 beat other deep learning methods in terms of recall, accuracy, and precision on a test dataset of 1930 images.

An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population

2023-10-03T09:42:40+00:00

INTRODUCTION: Cardiovascular disease is a major concern and pressing issue faced by the healthcare sector globally. According to a survey conducted by the WHO every year, CVDs cause 17.9 million deaths worldwide. Lack of pre-prediction of CVDs is a significant factor contributing to the death of patients. Predicting CVDs is a challenging task for medical practitioners as it requires a high level of medical analysis skills and extensive knowledge.

OBJECTIVES: We believe that the improvement in the accuracy of prediction can significantly reduce the risk caused by CVDs and help medical practitioners better diagnose patients .

METHODS: In this study, We created a CVD prediction model. using a ML approach. We utilized various algorithms, including logistic regression, Gaussian Naive Baye, Bernoulli Naive Baye, SVM, KNN, optimized KNN, X Gradient Boosting, and random forest algorithms to analyze and predict CVDs.

RESULTS: Our developed prediction model achieved an accuracy of 96.7%, indicating its effectiveness in predicting CVDs. DL algorithms can also assist in identifying, classifying, and quantifying patterns of medical images, improving patient evaluation and diagnosis based on prior medical history and evaluation patterns.

CONCLUSION: Furthermore, deep learning algorithms can help in developing new drugs with minimum cost by reducing the number of clinical research trials, using prior prediction of the drug's efficacy.

Stacking Model for Heart Stroke Prediction using Machine Learning Techniques

2023-10-03T12:40:14+00:00

The paper presents an adaptive model that utilized the machine learning algorithms to predict the heart diseases. As heart disease is one of the leading causes of death and understanding its mechanism, effective prevention, diagnosis, and treatment is very crucial. With the help of data analytics, machine learning, artificial intelligence, it is possible to provide optimal solution to the heart diseases. But still getting optimal accuracy is a challenging issue. Identifying the data pattern, correlation and algorithms affects the accuracy very much. In this work, a stacking model has been proposed to find the best models out of it and validate the model for better prediction accuracy. The model is stacked with seven algorithms different machine learning algorithms such as Radom Forest, Naïve Bayes, Linear Regression, Decision Tree, Ad boost, K Nearest Neighbour, and Gradient Boosting. The experiment was carried out with a training and testing ration of 80:20 in ration. Evaluations are carried out in different measures such as Precision, Recall, F Score, and Accuracy to demonstrate the efficiency of the algorithms. Form the experimentation it is observed that the gradient boosting outperforms the other competitive approaches as this algorithm combines weak predictive models to form a stronger ensemble model that can make highly accurate predictions with an accuracy of 94.67 percentages.

An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

2023-09-19T13:39:52+00:00

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient..

OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions.

METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance..

RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models.

CONCLUSION: Thus, the study concluded that involving hybridized ML algorithms in medical datasets increased predictions performance as it removed the challenges of high variance, low accuracy and feature noise and biases of medical datasets. The paper concluded that ensemble classifiers should be considered to improve sickle cell disease predictions.

Impact of Covid-19 epidemic on online learning and educational resources in China

2023-08-21T11:45:17+00:00

Online education was widely launched worldwide with the increasing impact of Covid-19 and multiple online platforms were developed or improved due to their demands. Several surveys have been conducted to analyze the Covid-19 impact on education, revealing the importance of information and communication technology (ICT). This epidemic has significantly changed all education levels and dramatically increased online learning. Online learning has various benefits with few drawbacks such as resources, economic effects, time, travelling, and so on. In this paper, we describe the impact of Covid-19 on education in China, the education of international students, problems in online learning, and the supportive technologies during this epidemic. Distance learning has been studied for years, expressed that it enhances the learners with lower paybacks; therefore, it was diminished dramatically. All these concerns will help us to understand global reforms and situations. We have also described affected regions, virus types, the Covid-19 cycle, and procedures to secure our education systems. Furthermore, we have highlighted some key issues of biosafety that will support the community to understand the standard procedures for developing a safe environment. Teachers play a key role in the development of a nation, and this study also enlightened the perspectives which should be addressed in future research.

An efficient and secure mutual authentication protocol in wireless body area network

2023-07-13T09:07:23+00:00

Wireless Body Area Network (WBAN) is an emerging field which is gaining a lot of attention in healthcare sector. It facilitates remote monitoring by gathering health related data using wearable bio-sensors based on IOT. This technological advancement would significantly improve the tracking of fitness, health care delivery, medical diagnostics, early disease prediction, and associated medical dealings of any individual. Several challenges persist in WBAN due to its openness and mobility. The medical data is extremely sensitive and personal in nature therefore it must be protected at any cost while being communicated between nodes. Highly resource constrained tiny sized bio-sensors restrict the usage of energy seeking traditional cryptographic techniques and hence require new methods to be evolved to secure the communication. The current study proposes a lightweight mutual authentication based key agreement scheme which is dependent on XOR operations and cryptographic hash functions. BAN logic is used for formal verification and automatic security verification tool Scyther is used for the analysis of security protocol. Proposed scheme is compared with other related works on 15 key security parameters which are identified on the basis of literature survey. The results indicate that the proposed scheme follows all the security parameters and performs better in terms of computation cost, energy consumption, communication cost and storage requirement as compared with other schemes.

An Efficient Discrete Wavelet Transform Architecture with Low Power and Multiplier-Less Structure for Pervasive Biomedical Image Processing Application

2023-03-23T15:07:44+00:00

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

2023-03-30T11:25:56+00:00

The diagnosis of diseases associated to the retina is significantly aided by retinal fundus images. However, when flash illumination is used during image acquisition, specularity reflection can occur on images. The retinal image processing applications are popular now days in diseases detection such as glaucoma, diabetic retinopathy, and cataract. Many modern disease detection algorithms suffer from performance accuracy limitation due to the creation of specularity reflection problem. This research proposes a hybrid model for screening of glaucoma which includes a preprocessing step to separate specular reflections from corrupted fundus images, a segmentation step using modified U-Net CNN, a feature extraction step, and an image classification step using support vector machine (SVM) with different kernels. Firstly, the diffuse and specular components are obtained using seven existing methods and apply a filter having high emphasis with a function called similar in each component. The best method, which provides highest quality images, is chosen among the seven compared methods and the output image is used in next steps for screening of glaucoma. The experimental results of the proposed model show that in preprocessing step, maximum improvement in terms of PSNR and SSIM are 37.97 dB and 0.961 respectively. For glaucoma detection experiment the results have the accuracy, sensitivity, and specificity of 91.83%, 96.39%, and 95.37% respectively and AUROC of 0.971.

Cancer disease multinomial classification using transfer learning and SVM on the genes’ sequences

2023-07-10T14:39:59+00:00

INTRODUCTION: Early disease detection plays an important role in medical field especially for cancer disease, which helps doctors in diagnosing and identifying the therapeutic process. Aiming to provide assistance, many biological techniques other than machine and deep learning models were proposed. They were applied on a different type of data such as medical images and clinical data. Despite the efficiency of those techniques, they remain costly and need a lot of execution and preparation time, and resources.

OBJECTIVES: In this paper, we present a novel method of disease detection analyzing the genes sequences composition.

METHODS: We start by extracting k-mer nucleotides as features from gene sequences with the Frequency Chaos Game Representation (FCGR) technique. Since extracted data are huge, we use a DeepInsight model to extract the most representative k-mers.

A combination of a transfer learning model, which is Residual neural Network (ResNet), and a support vector machine (SVM) algorithm is then used then to classify samples into 18 cancer disease types.

RESULTS: We achieved an accuracy of 0.98 while choosing FCGR₆ in feature extraction, and a combination of ResNet50 and SVM in the multinomial classification step, against an accuracy of 0.97 while using ResNet50 with a fully connected layer and FCGR₅.

CONCLUSION: Defining the gene sequence alterations helps in the disease detection at early stage. Here, we adopt the FCGR method (that gives the frequency of each k-mer) in defining features of the gene sequences. Then, we use deep learning models to deal with the big number of characteristics and predicting different cancer diseases.

Blockchain for IoT-enabled Healthcare

2023-06-28T21:38:45+00:00

Emerging technologies including such Internet of Things (IoT) and blockchain contribute significantly to the improvement of health services. The purpose of this chapter is to achieve and democratize services through the provision of medical care as a service. The result was the development of medical gadgets integrating healthcare sensors. It links medical equipment like the temperature controller to the cloud environment of medical doctors and staff. This study introduced the combination of IoT and Blockchain as a secure platform to reduce the scarcity of nurses. Blockchain was employed for storing and validating patient information in the proposed operating framework. A significant reduction in nursing gaps for large-scale patients has been shown. All technological specifications have been given to allow the prototyping execution of these suggested medical services simply adaptable. This article deals with Blockchain technology inclusion in Remote Medical Monitoring Devices Internet of Things (IoT) security. The document provides the advantages of Blockchain based safety methods and practical barriers in remote health monitoring via IoT devices. The study also examines several cryptographic methods appropriate for IoT implementation.

Teachers' Lifestyle who use ICTs in Basic Education during COVID-19: A case study

2023-09-19T13:44:53+00:00

Introduction: lifestyle is an indicator that refers to a set of behaviors and behaviors known as habits that people adopt, and these can be good or bad and this will depend on the life condition of the individual.

Aim: to determine the lifestyle of teachers who use ICTs in basic education during COVID-19 in North Lima.

Methods: a quantitative, descriptive-cross-sectional study, consisting of a total of 160 patients attending a health facility, who answered a questionnaire of sociodemographic data and the fantastic questionnaire.

Results: 5% of the participants have a lifestyle is in danger, 8.1% have a bad lifestyle, 52.5% have a regular lifestyle, 16.3% good lifestyle and 18.1% excellent lifestyle. With respect to the dimension family and friends that, 88.1% have an excellent lifestyle and 11.9% regular lifestyle. With respect to the physical activity dimension, 86.9% have an excellent lifestyle, 2.5% a good lifestyle and 10.6% a bad lifestyle.

Conclusions: It is concluded that educational counseling should be implemented on how to maintain a healthy lifestyle and how to prevent risk behaviors that harm health. It is concluded that health should be promoted, since it allows educating people to put into practice how to improve their lifestyle and how to have a healthy diet.

Detection of Covid-19 Using AI Application

2023-06-28T21:38:42+00:00

INTRODUCTION: In December of 2019, the infection which caused the pandemic started in the Hubei territory of Wuhan, China. They were identified as SARS-CoV-2, a highly infectious, easily transmissible virus that has caused an increasing number of deaths worldwide. Covid can be perceived with a testing strategy known as RT-PCR. As of now, this technique is broadly utilized for identifying the infection.

OBJECTIVES: The imaging modalities are utilized for various degrees of seriousness from asymptomatic to basic cases. Side effects of an individual contaminated with COVID-19 incorporate gentle hack, fever, chest torment, weakness, and so forth An individual with an extremefundamental ailment requires basic consideration. Imaging has assumed a larger part during the flare-up, with CT being a better option than invert transcriptase-polymerase chain response testing.

METHODS: With artificial intelligence and robotics, a variety of devices and solutions have been introduced to improve contactless service forhumans. The presentation of AI technology may be a distinct advantage for the contactless treatment of patients. Information technology and AI could solve the testing and tracking system without any human interaction.

RESULTS: CT imaging methods permit radiologists and doctors to distinguish inner structures and see their shape, size, thickness, and surface,which could help in the early discovery of asymptomatic cases.

CONCLUSION: This detailed information data can be utilized to decide whether there's a clinical issue, provide the extent and accurate area of the matter, and uncover other significant details which will assist the doctor with deciding the best treatment.

An Empirical Study on Classification of Monkeypox Skin Lesion Detection

2023-05-29T14:29:43+00:00

INTRODUCTION: After the covid-19 outbreak, Monkeypox has become a global pandemic putting people’s lives in jeopardy. Monkeypox has become a major concern in 40+ countries apart from Africa as scientists are struggling to clinically diagnose the virus as it looks similar with chickenpox and measles. As a part of our research, we found that to get the clinically tested result of monkey pox through polymerase chain reaction (PCR) test would take 3-4 days which is a lengthy process.

OBJECTIVES: The objective of this paper is to provide a rapid identification solution which can instantly detect monkeypox virus with the help of computer vision architectures. This can be considered for preliminary examination of skin lesions and help the victim isolate themselves so that they would be cautious and can stop the spreading of virus.

METHODS: Many studies have been conducted to identify the monkeypox with the help of Deep Learning models but in this study, we compare the test results obtained by deep learning CNN models AlexNet, GoogLeNet using transfer learning approach and determine the efficient model[2].

RESULTS: Testing the algorithms by changing the batch sizes and number of epochs we have obtained a highest accuracy of 83.61% for AlexNet and 82.64% for GoogLeNet.

CONCLUSION: AlexNet was outperforming GoogLeNet architecture in terms of validation accuracy thus providing better results.

Automated Cardiovascular Disease Prediction Models: A Comparative Analysis

2023-05-29T14:29:40+00:00

INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease.

OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest.

METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest.

RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%.

CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.

Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

2023-06-09T12:37:57+00:00

Various driver’s vigilance estimation techniques currently exist in the literature. But none of them estimates the driver’s vigilance in the complexity domain. In this research, we propose the recently introduced multivariate multiscale entropy method to fill the above mentioned research gap. We apply this technique to differential entropy features of electroencephalogram and electrooculogram signals to detect driver’s vigilance. Also, we employ it to the percentage of eye closure values to analyse the driver’s cognitive states (awake, tired and drowsy) in the complexity domain. The contribution of this research is to efficiently classify the driver’s cognitive states using a new feature based on multivariate multiscale entropy. The experimental complexity profile curves show the statistically significant differences (p < 0.01) among brain electroencephalogram, forehead electroencephalogram and electrooculogram signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the support vector machine, a machine learning technique, discriminates the driver’s cognitive states with a promising classification accuracy of 76.2%. Therefore, the complexity profile of driver’s cognitive states could be an indicator for vigilance estimation.

Quality of Life in Elderly University professors who attend a health establishment in Peru

2023-09-19T13:11:23+00:00

Introduction: The quality of life in the elderly is fundamental in which it allows good physical and mental health, with the purpose that it can develop its activities and that it can relate socially.

Aim: to determine the quality of life in the elderly university professors who attend a health establishment in Lima.

Methods: a quantitative, descriptive-transversal study, with a total population of 153 older adults, who answered a questionnaire on sociodemographic data and the WHOQOL-OLD quality of life questionnaire.

Results: we can see in terms of quality of life that 11.1% of the participants had a poor quality of life, 77.1% moderately good quality of life and 11.8% good quality of life. With respect to the sensory capacity dimension, 13.7% of the participants have a good quality of life, 73.2% moderately good quality of life and 13.1% poor quality of life. With respect to the autonomy dimension that, 15.7% of the participants have a good quality of life, 64.1% moderately good quality of life and 20.3% poor quality of life.

Conclusions: It is concluded that strategies or campaigns should be implemented that allow comprehensive care in the elderly in a preventive and promotional way for their health. It is concluded that older adults should be followed up to observe any condition that may decrease their quality of life. It is concluded that educational talks should be held for older adults on how to have a stable quality of life.

Support vector machine with optimized parameters for the classification of patients with COVID-19

2023-06-20T06:08:24+00:00

Introduction. The COVID-19 pandemic has had a significant impact worldwide, especially in health, where it is crucial to identify patients at high risk of clinical deterioration early.

Objective. This study aimed to design a model based on the support vector machine (SVM) algorithm, optimizing its parameters to classify patients with suspected COVID-19.

Methodology. One thousand patient records from two health establishments in Peru were used. After applying data preprocessing and variable engineering, the sample was reduced to 700 records. The construction of the model followed a machine learning methodology, using the linear, polynomial, sigmoid, and radial kernel functions, along with their estimated optimal parameters, to ensure the best performance.

Results. The results revealed that the SVM model with the linear and sigmoid kernels presented an accuracy of 95%, surpassing the polynomial kernel with 94% and the radial kernel (RBF) with 94%. In addition, a value of 0.92 was obtained for Cohen's kappa, which measures the degree of agreement between the predictions of the machine learning model and the actual results, which indicates an excellent deal for the linear and sigmoid kernel.

Conclusions. In conclusion, the SVM model with linear and sigmoid kernels could be a valuable tool for identifying patients at high risk of clinical deterioration in the context of the COVID-19 pandemic.

Latin American research on cardiovascular diseases: A bibliometric-network approach

2023-07-17T13:34:31+00:00

The increase in the global prevalence of cardiovascular diseases has raised great concern among health professionals worldwide, making the advancement of knowledge in this field even more important. The aim of the study was to perform a bibliometric analysis of the scientific literature in Latin America on cardiovascular pathologies from 2003 to 2023. The methodology was based on a bibliometric and quantitative analysis of the literature. The scientific production indicators were generated from 6660 documents selected from Scopus using keywords in English ("diseases" and "pathologies"). The number of publications devoted to the subject increased by 89% between 2009 and 2022. Brazil is the country with the highest scientific production (63.9%), and the Universidade de São Paulo with the most publications (n=1277). The journal Arquivos Brasileiros de Cardiologia had 684 publications, with Lotufo, P.A. (n=60) being the author with the most papers. It is concluded that cardiovascular diseases are important, as they are the main cause of disability and premature death, and both have an impact on increasing the cost of medical care. As a consequence, there has been an increase in the number of medical studies dedicated to this pathology in Latin America and the rest of the world.

Out of pocket and catastrophic health spending in Mexico in the face of the COVID-19 pandemic

2023-09-05T08:14:15+00:00

INTRODUCTION: The measurement of the financial coverage of a health system uses key indicators such as household out-of-pocket spending as well as catastrophic health spending. Said indicators depend on the financing structure of the health system as well as quality criteria and efficiency of the system in patient care. In the case of Mexico, in recent years there have been important changes in the structure of the health system in addition to suffering from the COVID-19 pandemic events that have significantly impacted the access to health of patients. Therefore, it is relevant to quantify the impact of these events on out-of-pocket spending and catastrophic spending on health in Mexico and have a robust diagnosis of the financial coverage of the system public health in Mexico.

OBJECTIVES: The main objective of this study is to quantify out-of-pocket spending and catastrophic spending on health in Mexican households for the year 2020. Comparing these estimates with previous years given the recent changes in the Mexican health system as well as the effect of the COVID-19 pandemic in these indicators.

METHODS: Based on the information available in the 2020 National Household Income and Expenditure Survey (ENIGH), out-of-pocket and catastrophic spending on health were estimated following the methodology proposed by the World Health Organization. A quantile regression was estimated to assess the effect of income distribution on out-of-pocket spending.

RESULTS: In Mexico in 2020, 67.7% (24.2 million) of households had an out-of-pocket health expenditure (OOHE) and 6% of these households had a catastrophic health expenditure (CHE), with respect to all households this percentage represents 4.04%. According to the classification stipulated by the World Health Organization, healthcare has six expenditure components: orthopedics, medicines, maternity, hospital, alternative medicines, and ambulatory expenses. The three main expenditure was attributable to drugs (39.9%), ambulatory (25.3%), and hospital costs (20.3%).

CONCLUSION: The effect of recent modifications to the public health system in Mexico in addition to the COVID-19 pandemic has been reflected in an increase in the percentage of households with out-of-pocket spending in Mexico, as well as the percentage of households with catastrophic spending in health. The main expense item is made in medicines, ambulatory care follow-up and hospitalization. It is a priority to establish efficient financial protection schemes that allow reversing this situation in terms of efficient access to health in Mexico.

Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets

2023-08-02T09:42:37+00:00

BACKGROUND: Though smartphone is popular and loneliness is higher among the youth, in low-and-middle income countries (LMICs) such as Bangladesh, the relation of loneliness with actual app usage is unexplored amid pandemic. Also, the studies conducted in developed countries are limited by exploration of some app categories.

METHODS: We conducted two studies in Bangladesh: in 2020 (N₁=100) and 2021 (N₂=105). We collected participant’s ULS-8 score and 7 days’ actual app usage. We extracted app usage behavioral data from 1.69 million events and did semi-partial and partial correlation analyses.

RESULTS: Our analysis did not present any significant relation which may indicate a negative impact on loneliness. However, we found higher usage of Social Media, Communication, Education, Books, and Shopping apps and higher entropy of Browser apps had significant (q<.05) relation with lower loneliness.

CONCLUSION: Smartphone may not negatively impact loneliness. Instead, some app categories can play a role to mitigate loneliness.

Role of biodentine in endodontics: a bibliometric and scientometric analysis

2023-09-07T10:32:31+00:00

Objective. Vital Pulp therapy using Biodentine has advanced, introducing and allowing new procedures and treatments, hence medical education should focus on research and publication. The aim of the study was to perform a bibliometric and scientometric analysis of the literature on the role of biodentine in endodontics from 2013 to 2023.

Methodology: A bibliometric and scientometric quantitative study formed the basis of the methodology. Scientific production indicators were generated from 87 documents selected from Scopus using English keywords ("Biodentine", "Endodontic").

Results: Since 2016, the number of papers published on this topic increased (69%), indicating a growing interest towards this material. Brazil is the country with the highest scientific interest (19%), and the Universidade Estadual Paulista Júlio de Mesquita Filho with the most publications (n=9). The International Endodontic Journal received 344 citations, Tanomaru-Filho M. (n=6) being the most cited.

Conclusion: It is concluded that the role of biodentine in endodontics has grown not only in production and authorship, but also in scope and medical research, incorporating these resources in various scenarios and clinical settings.

Deep Learning Framework for Identification of Skin Lesions

2023-09-19T08:08:10+00:00

Skin ailments don't just affect the physical appearance of an individual but also lead to psychological issues. Vitiligo and discoloration patches are such conditions that can negatively impact one's self-assurance. Here, authors have designed 14 distinct models to classify skin lesions using the HAM10000 dataset which is sorted into 7 classes including Actinic Keratosis, Melanocytic nevi, Actinic keratoses, Melanoma, Benign keratosis-like lesions, Basal cell carcinoma, and Vascular lesions. Further, authors compared their model against other state-of-the-art models, and additional-ly employed various pre-trained models like Resnet50, InceptionV3, MobileNetV2, Densenet201, VGG16, VGG19, InceptionResnetv2, Xception, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, Effi-cientNetB4, EfficientNetB5 that were trained on image net datasets. Their primary aim was to develop a framework that can be implemented in real-world applications using Efficient Nets. Experimental evaluations have shown that their proposed models have outperformed traditional pre-trained models like ResNets and VGG16 in terms of accuracy, precision, re-call, and validation loss, despite being lightweight. Interestingly, this im-provement was achieved without any data augmentation techniques. The authors achieved accuracy above 90% for all the EfficientNet models (B0-B5), which was far better than the existing pre-trained models, thus establishing the supremacy of proposed model.

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

2023-09-19T11:57:57+00:00

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.

Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.

Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.

OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.

METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.

RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.

CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Nursing Care through ICTs in Hypertensive Teachers with Cardiovascular Risk in a Primary Care Centers

2023-09-19T13:29:42+00:00

Introduction: Cardiovascular diseases (CVD) are a group of disorders of the heart and blood vessels and are the leading cause of death worldwide. In turn, they seem to affect men and women differentially, being the most frequent cause of death in the latter worldwide, even in developing countries.

Aim: to determine Nursing care using ICTs in hypertensive teachers with cardiovascular risk in a primary care center in North Lima. primary school in North Lima.

Methods: a quantitative, descriptive-cross-sectional study, with a population of 265 participants who answered a questionnaire of sociodemographic aspects and the cardiovascular risk calculator.

Results: we could observe that 4.2% have very high cardiovascular risk, 10.9% high cardiovascular risk, 37.4% moderate cardiovascular risk and 47.5% very low cardiovascular risk.

Conclusions: the person with high blood pressure should be educated about the risks that the disease can generate, and how to minimize the risks that may affect their health well-being.

Exergaming Characteristics in Interventions Addressing Physical Activity and Nutrition: A Systematic Literature Review

2023-02-05T10:44:33+00:00

INTRODUCTION: The increasing popularity of exergames to promote the adoption of physical activity and healthy nutrition among different population groups is well established. However, due to the use of various types of exergames, their effectiveness in addressing specific behaviours varies.

OBJECTIVES: This systematic review aims to identify, classify exergaming elements, and examine their efficacy in enhancing physical activity levels, improve nutrition habits, or a combination of both, across various populations.

METHODS: A systematic search was conducted to identify relevant publications. Data on study characteristics pertaining to types of exergames, purpose, focus, target population, technologies used, and the theoretical framework were extracted. A classification scheme of exergaming components and characteristics has been developed to facilitate this systematic review.

RESULTS: A total of 34 studies were included and n=21 of them were experimental. Most studies (n=31) were focused on Physical Activity using exergames, whereas n=9 studies addressed both Physical Activity and Nutrition simultaneously.

CONCLUSION: All of the studies reported positive behavioural outcomes, although, prolonged and sustained engagement with exergames were not consistently reported.

A Systematic Review on the Adoption of Blockchain Technology in the Healthcare Industry

2023-04-20T13:51:24+00:00

INTRODUCTION: Blockchain technology is a distributed ledger, decentralized, and cryptographically secure technology which has garnered considerable interest in different sectors including healthcare. It can enable better trust, security, management, and transparency of healthcare data, processes, and transactions resulting improving quality of care. Despite the fact of the increasing number of research investigating the applications/potentials of blockchain in healthcare, there is a scarcity of comprehensive reviews that focuses on the factors that influence its adoption in the healthcare industry.

OBJECTIVES: This review aims to summarise existing studies regarding the adoption of blockchain technology in the healthcare industry. This review presents a detailed review of existing empirical studies investigating the factors influencing blockchain adoption in healthcare by highlighting the research methodologies, targeted stakeholders, adoption theories/models used, and the influential factors explored in each of these studies. Careful syntheses of these studies would enable researchers and partitioners to acquire a wide knowledge and understand various opportunities and challenges of blockchain implementation in healthcare.

METHODS: Inspired on “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines, the study's scope and research questions are established, Scopus database is selected as an information resource, search strategy, and inclusion and exclusion criteria for document selection is developed. This review was conducted in August 2022. From 223 articles found in the search, 12 met the eligibility criteria and were selected to be extensively analyzed in this review.

RESULTS: This review reveals that very few empirical studies exist that sought to explore the significant factors influencing blockchain adoption in healthcare. The qualitative method was the most method employed, healthcare providers were the most targeted stakeholders, and most of the studies were not based on adoption theories/models. Privacy, government regulation, and trust were the most influential factors investigated in the studies.

CONCLUSION: The utilization of blockchain can help handle many issues in healthcare systems and bring improved healthcare delivery. Little attention has been paid to highlight internal and external factors that would impact successful blockchain adoption in healthcare. Additionally, the evaluated research placed little attention on understanding how underlying factors interact, social structures and institutional mechanisms affect the adoption of blockchain in healthcare. The reasons why healthcare organizations are hesitant to implement blockchain are still not clear. There is a need to conduct more research to examine the factors influencing the decision of healthcare stakeholders to adopt blockchain by using adoption theories/models. The proposed framework of the factors in this study may contribute as a starting point for future blockchain adoption studies in the healthcare industry.

Telemedicine and mHealth Applications for Health Monitoring in Rural Communities in Colombia: A Systematic Review

2023-05-27T21:05:52+00:00

INTRODUCTION: Telemedicine and mHealth applications constitute a central pillar in the digital transformation of healthcare.

OBJECTIVE: To describe the efficacy, applicability, and impact of telemedicine and mHealth applications on the monitoring and improvement of health in rural communities in Colombia.

METHODS: This research was carried out as a systematic review, a type of study that allows for a thorough and replicable evaluation of the existing literature in the databases PubMed, Scopus, Embase, Web of Science, Cochrane Library, CINAHL, and ERIC.

RESULTS: A total of 14 studies were included, which encompassed different types of research designs: two case-control studies, two randomized trials, four cross-sectional studies, two qualitative investigations, one consensus study, one retrospective cohort study, and two reviews. The sample size varied significantly among the studies, from 16 participants in the consensus study to 313,897 patients in one of the cross-sectional studies.

CONCLUSIONS: Telemedicine and mHealth applications are transforming the way medical care is delivered to rural communities in Colombia. These tools have proven to be valuable in improving the detection and management of chronic diseases such as cognitive decline and cardiovascular diseases. At the same time, the implementation of these technologies has shown to be effective in improving the quality of medical care, providing greater access to specialized medical services, and reducing the sense of isolation among health professionals in rural areas.

Use of real-time graphics in health education: A systematic review

2023-04-04T22:53:50+00:00

Introduction: Using real-time graphics in health education is particularly relevant in technical skill development and knowledge acquisition in surgery, emergency medicine, and nursing.

Objective: To systematize the literature on using real-time graphics in health education.

Methods: A systematic review was conducted in the databases: PubMed, Scopus, Embase, Web of Science, Cochrane Library, CINAHL, and ERIC.

Results: The impact of real-time graphics use, including virtual reality (VR), in health education was examined, covering disciplines such as medicine, nursing, and other related professions. The findings of the selected studies for this review and existing literature suggest that implementing real-time graphics technologies in health education can significantly improve learning and the acquisition of clinical skills compared to traditional approaches.

Conclusions: Virtual reality was found to be particularly effective in training technical skills and surgical procedures and improving the quality of teaching in various disciplines. These findings support experiential learning theory and the idea that repeated practice and immediate feedback in a safe and controlled environment are essential for skill acquisition.