Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis

Authors

Keywords:

Cardiovascular disease, KNN, SVM, Neural Network, Random Forest, Decision Tree, MLP, Logistic regression

Abstract

Heart disease has been a serious threat to mankind. According to research 7 out of 10 people die due to heart failure. In this paper, we have proposed a framework using which we can determine if a person has heart ailments or not. We have used various ML classification algorithms such as Logistic Regression, SVM, Random Forest, Decision tree, KNN, MLP, and Neural Network to determine the existence of heart disease. The best result has been obtained by Random Forest. Timely detection of a disease can save many people’s lives, thereby controlling the mortality rate to some extent.

References

[1] S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, Vol.7, pp.81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.

[2] Dinesh Kumar G, Arumugaraj K, Santhosh Kumar D and Mareeswari V, “Prediction of Cardiovascular Disease Using Machine Learning Algorithms”, Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India.

[3] D. Shah, S. Patel, and S. K. Bharti, “Heart Disease Prediction using Machine Learning Techniques,” SN Comput Sci, Vol.1, no.6, pp.345, Nov. 2020, doi: 10.1007/s42979-020-00365-y.

[4] R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, “Prediction of Heart DiseaseUsing a Combination of Machine Learning and Deep Learning,” Comput Intell Neurosci, Vol.2021, 2021, doi: 10.1155/2021/8387680.

[5] A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, R. Sun, and I. Garciá-Magarinõ, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mobile Information Systems, Vol.2018, 2018, doi: 10.1155/2018/3860146.

[6] M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison,” Comput BiolMed, vol. 136, Sep. 2021, doi: 10.1016/j.compbiomed.2021.104672.

[7] J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, Vol.8, pp.107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.

[8] A. K. Dwivedi, “Performance evaluation of different machine learning techniques for prediction of heart disease,” Neural Comput Appl, Vol.29, no.10, pp.685–693, May 2018, doi: 10.1007/s00521-016-2604-1.

[9] https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

[10]Archana Singh, Rakesh Kumar, “Heart Disease Prediction Using Machine Learning Algorithms”, 2020 International Conference on Electrical and Electronics Engineering (ICE3-2020)

[11] https://www.cdc.gov/heartdisease/facts.htm

Downloads

Published

2026-01-19

How to Cite

[1]
A. Datta and S. Ghosh, “Methodical Prediction of Cardiovascular Disease Using Consolidated Machine Learning Classification Algorithms and Analysis”, Int. J. Comp. Sci. Eng., vol. 11, no. 1, pp. 76–80, Jan. 2026.

Issue

Vol. 11 No. 1 (2023): Special Issue-1 Nov Editon

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.