Cardiovascular disease, especially heart disease, is one of the leading causes of global mortality. Annually, approximately 17.9 million people suffer from heart disease and accounting for over 80%of the deaths. So, predicting and detecting heart disease at an early stage is very important. As a result, medical professionals need to take appropriate and necessary actions at earlier stages. By applying machine learning technology, Healthcare professionals can diagnose cardiac conditions more accurately. Many researchers are focusing on developing intelligent systems that can accurately diagnose them using electronic health data, with the aid of machine learning (ML) algorithms. This study evaluates five machine learning algorithms, including Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF), to predict the likelihood of heart disease using the UCI Cleveland Heart Disease dataset. Data pre-processing and feature selection steps were done before building the models. The performance of this algorithm has been evaluated using accuracy, precision, recall, and F1-score. Further, the model performance has been shown through AUC and ROC curves. It is observed from the result that Logistic Regression performs better than other considered classification models with 88.52% accuracy, 90.62% recall, and 89.23% F1-score. However, in terms of precision, Naive Bayes performs better than the other considered models. These findings highlight how machine learning-based techniques can enhance the identification of early cardiovascular risk and help clinical decision-making.
| Published in | Mathematics and Computer Science (Volume 11, Issue 1) |
| DOI | 10.11648/j.mcs.20261101.12 |
| Page(s) | 6-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Heart Disease Prediction, Machine Learning Algorithms, Feature Selection, Medical Diagnosis
| [1] | Ufumaka, I. (2021). Comparative analysis of machine learning algorithms for heart disease prediction. Int. J. Sci. Res, 11, 339-346. |
| [2] |
W. H. Organization, “Health topics: Cardiovascular disease,”
https://www.who.int/health- topics/cardiovascul ar-diseases/ |
| [3] | Mukherjee, S., & Sharma, A. (2019). Intelligent heart disease prediction using neural network. International Journal of Recent Technology and Engineering, 30(5). |
| [4] | Choudhary, P., Satpathy, S., Dagur, A., & Shukla, D. K. (Eds.). (2025). Recent Trends in Intelligent Computing and Communication: Volume 1. CRC Press. |
| [5] | Bouqentar, M. A., Terrada, O., Hamida, S., Saleh, S., Lamrani, D., Cherradi, B., & Raihani, A. (2024). Early heart disease prediction using feature engineering and machine learning algorithms. Heliyon, 10(19). |
| [6] | Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, 81542-81554. |
| [7] | Narain, R., Saxena, S., & Goyal, A. K. (2016). Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Patient preference and adherence, 1259-1270. |
| [8] | Hasan, N., & Bao, Y. (2021). Comparing different feature selection algorithms for cardiovascular disease prediction. Health and Technology, 11(1), 49-62. |
| [9] | Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29(10), 685-693. |
| [10] | Alotaibi, F. S. (2019). Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications, 10(6). |
| [11] | Averbuch, T., Sullivan, K., Sauer, A., Mamas, M. A., Voors, A. A., Gale, C. P., ... & Van Spall, H.G. (2022). Applications of artificial intelligence and machine learning in heart failure. European Heart Journal-Digital Health, 3(2), 311-322. |
| [12] | Sarah, S., Gourisaria, M. K., Khare, S., & Das, H. (2022). Heart disease prediction using core machine learning techniques-a comparative study. In Advances in Data and Information Sciences: Proceedings of ICDIS 2021 (pp. 247- 260). Singapore: Springer Singapore. |
| [13] | Ganesh Chandra, M. M., Harshitha, A., Kiran, U., & Harsha Vardhan, N. (2022). Effective heart disease prediction using hybrid machine learning techniques. International Journal of Scientific Research in Engineering and Management (IJSREM), 6(4). |
| [14] | Acharya, A. (2017). Comparative study of machine learning algorithms for heart disease prediction. |
| [15] | Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., ... & Min, J. K. (2019). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European heart journal, 40(24), 1975-1986. |
| [16] | Aburayya, R. A., Alomar, R. A., Alnajjar, D. K., Athamnah, S., Alquran, H., Mustafa, W. A., Alkhayyat,A. (2023, July). Automated heart diseases detection using machine learning approach. In 2023 6th International Conference on Engineering Technology and its Applications (IICETA) (pp. 108-114). IEEE. |
| [17] | Srivastava, J., Bhargva, L., Yadav, D. J. P., Saxena, P., & Aggarwal, P. K. (2023, November). Automated heart disease prediction system using machine learning approaches. In 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI) (Vol. 1, pp. 1-6). IEEE. |
| [18] | Ganie, S. M., Pramanik, P. K. D., & Zhao, Z. (2025). Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets. Scientific reports, 15(1), 13912. |
| [19] |
APA Style
Babu, M. G. R., Alamin, M., Hossain, S., Mohiuddin, A. M. (2026). A Comprehensive Analysis of Five Machine Learning Models for Predicting Heart Disease. Mathematics and Computer Science, 11(1), 6-16. https://doi.org/10.11648/j.mcs.20261101.12
ACS Style
Babu, M. G. R.; Alamin, M.; Hossain, S.; Mohiuddin, A. M. A Comprehensive Analysis of Five Machine Learning Models for Predicting Heart Disease. Math. Comput. Sci. 2026, 11(1), 6-16. doi: 10.11648/j.mcs.20261101.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.mcs.20261101.12,
author = {Md. Golam Rabbanie Babu and Md Alamin and Sourab Hossain and A. M. Mohiuddin},
title = {A Comprehensive Analysis of Five Machine Learning Models for Predicting Heart Disease
},
journal = {Mathematics and Computer Science},
volume = {11},
number = {1},
pages = {6-16},
doi = {10.11648/j.mcs.20261101.12},
url = {https://doi.org/10.11648/j.mcs.20261101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20261101.12},
abstract = {Cardiovascular disease, especially heart disease, is one of the leading causes of global mortality. Annually, approximately 17.9 million people suffer from heart disease and accounting for over 80%of the deaths. So, predicting and detecting heart disease at an early stage is very important. As a result, medical professionals need to take appropriate and necessary actions at earlier stages. By applying machine learning technology, Healthcare professionals can diagnose cardiac conditions more accurately. Many researchers are focusing on developing intelligent systems that can accurately diagnose them using electronic health data, with the aid of machine learning (ML) algorithms. This study evaluates five machine learning algorithms, including Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF), to predict the likelihood of heart disease using the UCI Cleveland Heart Disease dataset. Data pre-processing and feature selection steps were done before building the models. The performance of this algorithm has been evaluated using accuracy, precision, recall, and F1-score. Further, the model performance has been shown through AUC and ROC curves. It is observed from the result that Logistic Regression performs better than other considered classification models with 88.52% accuracy, 90.62% recall, and 89.23% F1-score. However, in terms of precision, Naive Bayes performs better than the other considered models. These findings highlight how machine learning-based techniques can enhance the identification of early cardiovascular risk and help clinical decision-making.
},
year = {2026}
}
TY - JOUR T1 - A Comprehensive Analysis of Five Machine Learning Models for Predicting Heart Disease AU - Md. Golam Rabbanie Babu AU - Md Alamin AU - Sourab Hossain AU - A. M. Mohiuddin Y1 - 2026/02/04 PY - 2026 N1 - https://doi.org/10.11648/j.mcs.20261101.12 DO - 10.11648/j.mcs.20261101.12 T2 - Mathematics and Computer Science JF - Mathematics and Computer Science JO - Mathematics and Computer Science SP - 6 EP - 16 PB - Science Publishing Group SN - 2575-6028 UR - https://doi.org/10.11648/j.mcs.20261101.12 AB - Cardiovascular disease, especially heart disease, is one of the leading causes of global mortality. Annually, approximately 17.9 million people suffer from heart disease and accounting for over 80%of the deaths. So, predicting and detecting heart disease at an early stage is very important. As a result, medical professionals need to take appropriate and necessary actions at earlier stages. By applying machine learning technology, Healthcare professionals can diagnose cardiac conditions more accurately. Many researchers are focusing on developing intelligent systems that can accurately diagnose them using electronic health data, with the aid of machine learning (ML) algorithms. This study evaluates five machine learning algorithms, including Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF), to predict the likelihood of heart disease using the UCI Cleveland Heart Disease dataset. Data pre-processing and feature selection steps were done before building the models. The performance of this algorithm has been evaluated using accuracy, precision, recall, and F1-score. Further, the model performance has been shown through AUC and ROC curves. It is observed from the result that Logistic Regression performs better than other considered classification models with 88.52% accuracy, 90.62% recall, and 89.23% F1-score. However, in terms of precision, Naive Bayes performs better than the other considered models. These findings highlight how machine learning-based techniques can enhance the identification of early cardiovascular risk and help clinical decision-making. VL - 11 IS - 1 ER -
Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
Department of General Education, Canadian University of Bangladesh, Dhaka, Bangladesh
Information