Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease
DOI:
https://doi.org/10.4108/eetpht.10.5244Keywords:
Breast Heart Disease, Edge Detection Classification, Human Intelligence, SegmentationAbstract
INTRODUCTION: Preventable deaths from cardiovascular diseases outnumber all others combined. Detecting it at an early stage is crucial. Human lives will be saved as a result.
OBJECTIVES: Improved cardiac disease prediction using machine learning classifiers is the focus of this article.
METHODS: We have used many different classifiers, such as the support vector machine, naive bayes, random forest, and k-nearest neighbours, to achieve this goal, even though we can’t predict high accuracy in this classifier. So, we have proposed Hyper parameter adjustment was applied to the classifiers, which increased their precision. It was possible to compare the classifiers.
RESULTS: In comparison to other machine learning classifiers, Logistic Regression achieves higher prediction accuracy, at 95.5%.
CONCLUSION: To help people find the nearest cardiac care facilities, Google Maps has been integrated into a responsive web application that has been built for forecasting heart illness.
Downloads
References
Rehan Ahmed, Maria Bibi, Sibtain Syed. Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN Algorithms. International Journal of Computations, Information and Manufacturing. 2023; Vol. 3.1, pp 49-54. DOI: https://doi.org/10.54489/ijcim.v3i1.223
Jayachitra S, Aruchamy, P, Lebbe A:. An efficient clinical support system for heart disease prediction using TANFIS classifier. Computational Intelligence. 2022; Vol. 38:pp.610-640. DOI: https://doi.org/10.1111/coin.12487
Balasubramaniam S, Kumar, K, Kavitha, V: Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection.Computational intelligence and neuroscience. 2022; Vol. 2022:pp.1-18. DOI: https://doi.org/10.1155/2022/2819378
Kavitha, M, Roobini, S, Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems, Principles, Challenges and Applications, Machine Learning and Artificial Intelligence in Healthcare Systems. 2023; 25-56. DOI: https://doi.org/10.1201/9781003265436-2
Ghulab Nabi Ahamad, Shafiullah, Hira Fatima, Imdadullah, S. M. Zakariya, Mohamed Abbas, Mohammed S. Alqahtani and Mohammed Usman.: Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease. Processes. 2023; Vol. 11(3), pp. 734. DOI: https://doi.org/10.3390/pr11030734
Khondokar Oliullah, Alistair Barros, Md. Whaiduzzaman. Analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction. Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. 2023; Vol. 618, pp. 225-236. DOI: https://doi.org/10.1007/978-981-19-9483-8_19
Mohammad Reza Daliri. Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement. 2012; Vol. 45, pp. 1729–1734. DOI: https://doi.org/10.1016/j.measurement.2012.04.013
Karnika Dwivedi, Hari Om Sharan, Vinod Vishwakarma. Analysis of decision tree for diabetes prediction. International Journal of Engineering and Technical Research. 2019; Vol. 9, pp. 3-6. DOI: https://doi.org/10.31873/IJETR.9.6.2019.64
Kemal Polat, Salih Güneş. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing. 2007; Vol. 17, pp. 702–710. DOI: https://doi.org/10.1016/j.dsp.2006.09.005
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. Progressive neural architecture search. In Proceedings of the European conference on computer vision. 2018; pp. 19–34. DOI: https://doi.org/10.1007/978-3-030-01246-5_2
Margret Anouncia S, Clara Madonna L. J, Jeevitha P, Nandhini R. T. Design of a diabetic diagnosis system using rough sets. Cybernetics and Information Technologies. 2013; Vol. 13(3), pp. 124–139. DOI: https://doi.org/10.2478/cait-2013-0030
Peter J. Valdez, Vincent J. Tocco, Phillip E. Savage. A general kinetic model for the hydrothermal liquefaction of microalgae. Bioresource Technology. 2014; Vol. 163, pp. 123–127. DOI: https://doi.org/10.1016/j.biortech.2014.04.013
S. Muthukaruppan, M.J. Er. A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications. 2012; Vol. 39, pp. 11657-11665. DOI: https://doi.org/10.1016/j.eswa.2012.04.036
Mostafa Fathi Ganji, Mohammad Saniee Abadeh.: A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis. Expert Systems with Applications. 2011; Vol. 38(12), pp.14650-14659. DOI: https://doi.org/10.1016/j.eswa.2011.05.018
Akin Ozcift, Arif Gulten. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine. 2011; Vol. 104, no. 3, pp. 443–451. DOI: https://doi.org/10.1016/j.cmpb.2011.03.018
Quan Zou, Kaiyang Qu, Yamei Luo, Dehui Yin, Ying Ju, Hua Tang. Predicting diabetes mellitus with machine learning techniques. Frontiers in Genetics. 2018; Vol. 9(515). DOI: https://doi.org/10.3389/fgene.2018.00515
Wenbo Wang, Meng Tong, Min Yu. Blood glucose prediction with VMD and LSTM optimized by improved particle swarm optimization. IEEE Access. 2020; Vol. 8, pp. 217908–217916. DOI: https://doi.org/10.1109/ACCESS.2020.3041355
Md. Kamrul Hasan, Md. Ashraful Alam, Dola Das, Eklas Hossain, Mahmudul Hasan. Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access. 2020; Vol. 8, pp. 76516-76531. DOI: https://doi.org/10.1109/ACCESS.2020.2989857
Shaksham Kapoor, K Priya. Optimizing hyper parameters for improved diabetes prediction. International Research Journal of Engineering and Technology. 2018; Vol. 5(05).
Suyash Srivastava, Lokesh Sharma, Vijeta Sharma, Ajai Kumar, Hemant Darbari. Prediction of diabetes using artificial neural network approach. In Engineering Vibration, Communication and Information Processing, 2020; pp. 679-687. DOI: https://doi.org/10.1007/978-981-13-1642-5_59
T. Santhanam, M.S. Padmavathib. Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis. Procedia Computer Science. 2015; Vol. 47, pp. 76-83. DOI: https://doi.org/10.1016/j.procs.2015.03.185
Nongyao Nai-arun, Rungruttikarn Moungmai. Comparison of classifiers for the risk of diabetes prediction. Procedia Computer Science. 2015; Vol. 69, pp. 132-142. DOI: https://doi.org/10.1016/j.procs.2015.10.014
Aishwarya Mujumdar, V Vaidehi. Diabetes prediction using machine learning algorithms. Procedia Computer Science. 2019; Vol. 165, pp. 292-299. DOI: https://doi.org/10.1016/j.procs.2020.01.047
Vandana Roy, Prashant Kumar Shukla, Amit Kumar Gupta, Vikas Goel, Piyush Kumar Shukla, Shailja Shukla. Taxonomy on EEG artifacts removal methods, issues, and healthcare applications. Journal of Organizational and End User Computing. 2021; Vol. 33, pp. 19–46. DOI: https://doi.org/10.4018/JOEUC.2021010102
Geetanjli Khambra, Prashant Shukla. Novel machine learning applications on fly ash based concrete: an overview. Materials Today Proceedings. 2023; Vol. 80(3), pp. 3411-3417. DOI: https://doi.org/10.1016/j.matpr.2021.07.262
Prashant Kumar Shukla, Jasminder Kaur Sandhu, Anamika Ahirwar, Deepika Ghai, Priti Maheshwary, Piyush Kumar Shukla. Multiobjective genetic algorithm and convolutional neural network based COVID19 identification in chest X-ray images. Mathematical Problems in Engineering. 2021; Vol. 2021, pp. 1-9. DOI: https://doi.org/10.1155/2021/7804540
Neeraj Kumar Rathore, Neelesh Kumar Jain, Prashant Kumar Shukla, UmaShankar Rawat, Rachana Dubey. Image forgery detection using singular value decomposition with some attacks. National Academy Science Letters. 2021; Vol. 44, pp. 331–338. DOI: https://doi.org/10.1007/s40009-020-00998-w
Manish Agrawal, Asif Ullah Khan, Piyush Kumar Shukla. Stock price prediction using technical indicators: a predictive model using optimal deep learning. International Journal of Recent Technology and Engineering. 2019; Vol. 8(2), pp. 2297–2305. DOI: https://doi.org/10.35940/ijrte.B3048.078219
Vandana Roy, Shailja Shukla, Piyush Kumar Shukla, Paresh Rawat. Gaussian elimination-based novel canonical correlation analysis method for EEG motion artifact removal. Journal of Healthcare Engineering. 2017; Vol. 2017. DOI: https://doi.org/10.1155/2017/9674712
Rajendra Gupta, Piyush Kumar Shukla. Performance analysis of antiphishing tools and study of classification data mining algorithms for a novel anti-phishing system. International Journal of Computer Network and Information Security. 2015; Vol. 7, pp. 70–77. DOI: https://doi.org/10.5815/ijcnis.2015.12.08
Manish Kumar Ahirwar, Piyush Kumar Shukla, Rakesh Singhai. CBO-IE.: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy. Scientific Programming. 2021; Vol. 2021, pp.1-4. DOI: https://doi.org/10.1155/2021/8715668
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Saravanan Thangavel, Saravanakumar Selvaraj, Ganesh Karthikeyan V, K Keerthika
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
