Mahmood Ali Mirza; Jaddu Lohith Kumar; Gandavarapu Mohan Sai; Kongora Venkat Chowdary; Karnaati Kranthi Kiran; Faheem Ali Mirza

A Systematic Approach for Heart Disease Analysis using Machine Learning Algorithms

PDF (710KB), PP.214-225

Views: 0 Downloads: 0

Author(s)

Mahmood Ali Mirza ^1,* Jaddu Lohith Kumar ¹ Gandavarapu Mohan Sai ¹ Kongora Venkat Chowdary ¹ Karnaati Kranthi Kiran ¹ Faheem Ali Mirza ²

1. Department of Computer Science and Engineering , Krishna University College of Engineering and Technology, Krishna University, Rudravaram, Andhra Pradesh, India

2. Department of Information Technology, SIR C R Reddy College of Engineering, Eluru, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.02.14

Received: 3 Apr. 2025 / Revised: 23 May 2025 / Accepted: 14 Sep. 2025 / Published: 8 Apr. 2026

Index Terms

Data Mining, Classification, Heart disease, Regression, K Nearest Neighbors, Support Vector Model, Decision tree, and Random Forest, Gradient Boosting Classifier

Abstract

The number of heart disease patients has significantly increased in recent years, and heart illness is linked to a high death rate. Furthermore, as technology advanced, several sophisticated devices were created to assist patients in measuring their health at home and estimating their risk of developing heart disease. Using six machine learning models, the study seeks to determine how accurate self-measured physical health indicators are at predicting heart disease when compared to all indicators assessed by medical professionals. Logistics Regression, K Nearest Neighbors, Support Vector Model, Decision Tree, Random Forest, and Gradient Boosting Classifier were among the six models employed to forecast heart disease. Twelve different test findings and 1189 patients' heart disease risks are included in the database utilized for the study. While the metrics contains six outcomes that could be tested, the all metrics contained all twelve test results. The accuracy score and false negative rate were calculated for each of the five models that were built for the all metrics.The findings demonstrated that in all five models, all metrics had greater accuracy scores than existing metrics. For five machine learning models, all metrics had false negative rates that were either lower or equivalent to that of existing metrics. The results showed that all physical indicators were more accurate in predicting patients' risk of heart disease than metrics measured physical health indicators. Therefore, all physical health indicators are preferable for assessing the risk for cardiac illnesses in the absence of future development of indicators.

Cite This Paper

Mahmood Ali Mirza, Jaddu Lohith Kumar, Gandavarapu Mohan Sai, Kongora Venkat Chowdary, Karnaati Kranthi Kiran, Faheem Ali Mirza, "A Systematic Approach for Heart Disease Analysis using Machine Learning Algorithms", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.2, pp.214-225, 2026. DOI:10.5815/ijem.2026.02.14

Reference

[1]A. M. Mahmood and M. R. Kuppa, "Early Detection of Clinical Parameters in Heart Disease by Improved Decision Tree Algorithm," 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems, Warangal, India, 2010, pp. 24-29, doi: 10.1109/VCON.2010.12.
[2]Huating Sun, Jianan Pan "Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators", Journal of Data Analysis and Information Processing, Vol.11 No.1, 2023.
[3]Alkayyali, Z. K., S. Anuar Bin Idris, and Samy S. Abu-Naser. "A systematic literature review of deep and machine learning algorithms in cardiovascular diseases diagnosis." Journal of Theoretical and Applied Information Technology 101.4 (2023): 1353-1365.
[4]Haq, Amin Ul, et al. "A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms." Mobile information systems 2018.1 (2018): 3860146.
[5]A. M. Mahmood and M. R. Kuppa, "Improving the efficiency of biomarker identification using expert knowledge," Trendz in Information Sciences &Computing(TISC2010), Chennai, India, 2010, pp. 111-115, doi: 10.1109/TISC.2010.5714618.
[6]Mahmood, Ali Mirza, and MrithyumjayaRaoKuppa. "Increasing generalization accuracy by using multivariate statistical method." International Journal of Computer Systems Science & Engineering 26.2 (2011): 97-102.
[7]A. M. Mahmood and M. R. Kuppa, "Improving the efficiency of biomarker identification using expert knowledge," Trendz in Information Sciences &Computing(TISC2010), Chennai, India, 2010, pp. 111-115, doi: 10.1109/TISC.2010.5714618.
[8]G. Dinesh, Dr Ali MirzaMahmood , “A Novel Corona Virus Detection and Validation Measures using Machine Learning Techniques”A Advancement of Computer Technology and its Applications, Volume 5 Issue 1, 10.5281/zenodo.6476714
[9]Dr. Mahmood Ali Mirza,&Faheem Ali Mirza. (2025). Classification Approach for Analysis of Weather Dataset with Different Training Strategies. Recent Trends in Information
Technology and Its Application, 8(2), 4–13.https://doi.org/10.5281/zenodo.14997848.
[10]Thota, N. R., and Vasumathi, D. (2023). A Distinctive Ensemble Deep Learning Model for Brain Tumor MRI Image Classification. i-manager’s Journal on Artificial Intelligence & Machine Learning, 1(2), 12-21. https://doi.org/10.26634/jaim.1.2.19281
[11]NR Thota, D Vasumathi , “Wasserstein GAN-gradient penalty with deep transfer learning based Alzheimer disease classification on 3D MRI scans”, i-manager's Journal on Image Processing, Vol 9, Issue 4,, 2022
[12]NarasimhaRaoThota, D Vasumathi,” BRAIN TUMOR CLASSIFICATION WITH CONVLSTM USING NAKAGAMI PARAMETRIC IMAGING AND BAYESIAN FUZZY CLUSTERING”, International Journal of Engineering Applied Sciences and Technology, Vol 8, Issue 10,, 2024.
[13]Kumar, N. S., Rao, K. N., Govardhan, A., Reddy, K. S., &Mahmood, A. M. (2014). Undersampled K-means approach for handling imbalanced distributed data. Progress in Artificial Intelligence, 3(1), 29-38.
[14]Mahmood, A. M. A Classification Approach for Identification of Glass using C4. 5 Algorithm. Advancement of Computer Technology and its Applications Volume 7 Issue 3 e-ISSN: 2584-1262 DOI: https://doi.org/10.5281/zenodo.11401359.
[15]Mahmood, A. M. (2015). Class imbalance learning in data mining–a survey. International Journal of Communication Technology for Social Networking Services, 3(2), 17-38.
[16]Mahmood, A. M., Satuluri, N., &Kuppa, M. R. (2011). An Overview of recent and traditional decision tree classifiers in machine learning. International Journal of Research and Reviews in Ad Hoc Networks, 1(1), 2011.
[17]Mahmood, A. M., &Kuppa, M. R. (2010, December). A novel pruning approach using expert knowledge. In INTERACT-2010 (pp. 33-38). IEEE.
[18]Mirza, M. A., Babu, G. S., Ajay, G. P., Rohit, D., &Anand, C. BE-Abhaya: A Next Gen safety Application for Emergency Response and Risk Mitigation. Advancement of Computer Technology and its Applications Volume 8 Issue 2 e-ISSN: 2584-1262 DOI: https://doi.org/10.5281/zenodo.14970112
[19]Mahmood, A. M., Kowshik, B., &Mirza, F. A. Artificial Intelligence Techniques, Methods and Framework: An Epigrammatic Analysis. Advancement of Computer Technology and its Applications Volume 8 Issue 1 e-ISSN: 2584-1262 DOI: https://doi.org/10.5281/zenodo.13999765
[20]Tyagi, N., Jain, P. (2024). Heart Disease Prediction Using Machine Learning Algorithms. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. ICDIS 2024. Lecture Notes in Networks and Systems, vol 1127. Springer, Singapore.https://doi.org/10.1007/978-981-97-7360-2_5
[21]Mishra, A. et al. (2024). Heart Disease Prediction by Machine Learning. In: Mandal, J.K., De, D. (eds) Machine Learning for Social Transformation. EAIT 2024. Lecture Notes in Networks and Systems, vol 1131. Springer, Singapore. https://doi.org/10.1007/978-981-97-7532-3_25
[22]Ekle, F.A., Shidali, V., Ochogwu, R.E. et al. Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review. Discov Artif Intell 4, 113 (2024). https://doi.org/10.1007/s44163-024-00181-w
[23]KuldeepVayadande, ArnavDhiwar, Darpan Khadke, Rohan Golawar, Sarwesh Khairnar, Sarthak Wakchoure, Sumeet Bhoite. Published in: Techno-societal 2022, Publisher: Springer International Publishing
[24]Mallikarjunamallu, K., Syed, K. (2024). A Review on Heart Diseases Using Machine Learning and Deep Learning Techniques. In: Pant, M., Deep, K., Nagar, A. (eds) Proceedings of the 12th International Conference on Soft Computing for Problem Solving. SocProS 2023. Lecture Notes in Networks and Systems, vol 995. Springer, Singapore. https://doi.org/10.1007/978-981-97-3292-0_45
[25]G. Sakthipriya, Y. Suresh, C. Varnisha, R. Sindhu, R. Shivraj, Published in: Innovations in VLSI, Signal Processing and Computational Technologies, Publisher: Springer Nature Singapore
[26]Bhavekar, Girish&Goswami, Agam & Chafle, Pratiksha & Gaikwad, Amit & Vyawahare, Harsha. (2024). Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review. Multimedia Tools and Applications. 83. 86895-86922. 10.1007/s11042-024-19680-0.
[27]Amrit Singh, HarisankarMahapatra, Anil Kumar Biswal, Madhumita Mahapatra, Debabrata Singh, Milan Samantaray, Heart Disease Detection Using Machine Learning Models, Procedia Computer Science, Volume 235, 2024, Pages 937-947, ISSN 1877-0509,https://doi.org/10.1016/j.procs.2024.04.089.
[28]Breiman, Leo. "Bagging predictors." Machine learning 24 (1996): 123-140.
[29]Parhami, Behrooz. "Voting algorithms." IEEE transactions on reliability 43.4 (1994): 617-629.
[30]Schapire, Robert E. "A brief introduction to boosting." Ijcai. Vol. 99. No. 999. 1999.
[31]Rigatti, Steven J. "Random forest." Journal of Insurance Medicine 47.1 (2017): 31-39.

International Journal of Engineering and Manufacturing (IJEM)