Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models
DOI:
https://doi.org/10.4108/eetiot.5361Keywords:
Data analytics, healthcare, disease, prediction, machine learningAbstract
Development and exploration of several Data analytics techniques in various real-time applications (e.g., Industry, Healthcare Neuroscience) in various domains have led to exploitation of it to extract paramount features from datasets. Following the introduction of new computer technology, the health sector had a significant transformation that compelled it to produce more medical data, which gave rise to a number of new disciplines of study. Quite a few initiatives are made to deal with the medical data and how its usage can be helpful to humans. This inspired academics and other institutions to use techniques like data analytics, its types, machine learning and different algorithms, to extract practical information and aid in decision-making. The healthcare data can be used to develop a health prediction system that can improve a person's health. Based on the dataset provided, making accurate predictions in early disease prediction benefits the human community.
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M. Akhil jabbar, B.L. Deekshatulu, Priti Chandra, Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm, Procedia Technology, Volume 10, 2013, Pages 85-94, ISSN 2212-0173, https://doi.org/10.1016/j.protcy.2013.12.340. DOI: https://doi.org/10.1016/j.protcy.2013.12.340
D. Dahiwade, G. Patle and E. Meshram, "Designing Disease Prediction Model Using Machine Learning Approach," 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp. 1211-1215, doi: 10.1109/ICCMC.2019.8819782. DOI: https://doi.org/10.1109/ICCMC.2019.8819782
S. Ambesange, R. Nadagoudar, R. Uppin, V. Patil, S. Patil and S. Patil, "Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques," 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), 2020, pp. 1-6, doi: 10.1109/BHTC50970.2020.9297949. DOI: https://doi.org/10.1109/B-HTC50970.2020.9297949
D. Rahmat, A. A. Putra, Hamrin and A. W. Setiawan, "Heart Disease Prediction Using K-Nearest Neighbor," 2021 International Conference on Electrical Engineering and Informatics (ICEEI), 2021, pp. 1-6, doi: 10.1109/ICEEI52609.2021.9611110. DOI: https://doi.org/10.1109/ICEEI52609.2021.9611110
P. Deepika and S. Sasikala, "Enhanced Model for Prediction and Classification of Cardiovascular Disease using Decision Tree with Particle Swarm Optimization," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 1068-1072, doi: 10.1109/ICECA49313.2020.9297398. DOI: https://doi.org/10.1109/ICECA49313.2020.9297398
N. Rochmawati et al., "Covid Symptom Severity Using Decision Tree," 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), 2020, pp. 1-5, doi: 10.1109/ICVEE50212.2020.9243246. DOI: https://doi.org/10.1109/ICVEE50212.2020.9243246
A. M. Elsayad, M. Al-Dhaifallah and A. M. Nassef, "Analysis and Diagnosis of Erythemato-Squamous Diseases Using CHAID Decision Trees," 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018, pp. 252-262, doi: 10.1109/SSD.2018.8570553. DOI: https://doi.org/10.1109/SSD.2018.8570553
M. Pak and M. Shin, "Developing disease risk prediction model based on environmental factors," The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), 2014, pp. 1-2, doi: 10.1109/ISCE.2014.6884338. DOI: https://doi.org/10.1109/ISCE.2014.6884338
P. S. Kohli and S. Arora, "Application of Machine Learning in Disease Prediction," 2018 4th International Conference on Computing Communication and Automation (ICCCA), 2018, pp. 1-4, doi: 10.1109/CCAA.2018.8777449. DOI: https://doi.org/10.1109/CCAA.2018.8777449
M. Chakarverti, S. Yadav and R. Rajan, "Classification Technique for Heart Disease Prediction in Data Mining," 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2019, pp. 1578-1582, doi: 10.1109/ICICICT46008.2019.8993191. DOI: https://doi.org/10.1109/ICICICT46008.2019.8993191
S. Ambekar and R. Phalnikar, "Disease Risk Prediction by Using Convolutional Neural Network," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1-5, doi: 10.1109/ICCUBEA.2018.8697423. DOI: https://doi.org/10.1109/ICCUBEA.2018.8697423
J. Archenaa1 and E. A. Mary Anita 2A, “Survey Of Big Data Analytics in Healthcare and Government”, 8 May,2015 https://www.sciencedirect.com/science/article/pii/S1877050915005220
Wills, Mary J. Decisions Through Data: Analytics in Healthcare. Journal of Healthcare Management: July–August 2014 - Volume 59 - Issue 4 - p 254-262 DOI: https://doi.org/10.1097/00115514-201407000-00005
Bakot, K., Ślęzak, A. The use of Big Data Analytics in healthcare. J Big Data 9, 3 (2022). https://doi.org/10.1186/s40537-021-00553-4 DOI: https://doi.org/10.1186/s40537-021-00553-4
Advances in Mathematics: Scientific Journal 9 (2020), no.10, 8207–8215 ISSN: 1857-8365 (printed); 1857-8438 (electronic) https://doi.org/10.37418/amsj.9.10.50 Spec. Iss. on AOAOCEP-2020. DOI: https://doi.org/10.37418/amsj.9.10.50
T. N. Pandey, A. K. Jagadev, S. K. Mohapatra and S. Dehuri, "Credit risk analysis using machine learning classifiers," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 1850-1854, doi: 10.1109/ICECDS.2017.8389769. DOI: https://doi.org/10.1109/ICECDS.2017.8389769
Ahmed, U., Issa, G. F., Khan, M. A., Aftab, S., Khan, M. F., Said, R. A., ... & Ahmad, M. (2022). Prediction of diabetes empowered with fused machine learning. IEEE Access, 10, 8529-8538. DOI: https://doi.org/10.1109/ACCESS.2022.3142097
Arumugam, K., Naved, M., Shinde, P. P., Leiva-Chauca, O., Huaman-Osorio, A., & Gonzales-Yanac, T. (2023). Multiple disease prediction using Machine learning algorithms. Materials Today: Proceedings, 80, 3682-3685. DOI: https://doi.org/10.1016/j.matpr.2021.07.361
Biswas, N., Ali, M. M., Rahaman, M. A., Islam, M., Mia, M. R., Azam, S., ... & Moni, M. A. (2023). Research Article Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques. DOI: https://doi.org/10.1155/2023/6864343
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9.https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016
Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937
Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023.https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052
Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603
Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579
Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484
Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470
Hegde, S., & Mundada, M. R. (2021). Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach. International Journal of Pervasive Computing and Communications, 17(1), 20-36. DOI: https://doi.org/10.1108/IJPCC-04-2020-0018
Pandey, T.N., Mahakud, R.R., Patra, B., Giri, P.K., Dehuri, S. (2022). Performance of Machine Learning Techniques Before and After COVID-19 on Indian Foreign Exchange Rate. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_41. DOI: https://doi.org/10.1007/978-981-16-8739-6_41
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