Predicting Heart Disease with Machine Learning: A Comparative Analysis of Multiple Models

Authors

• Sonia Afroz, University of Information Technology & Sciences
• Md Munjurul Islam, UTeM University
• Nikunj Sharma, Delhi University
• Kanika Rawat, University of Delhi
• Tuhalika Saha, American International University Bangladesh
• Rana Das, Daffodil International University
• Sheikh Urvana Akter Mim, Bangladesh University
• Mst Sanjida Alam, Clark University
• Md. Akkas Ali, Multimedia University

Document Type

Conference Proceeding

Abstract

While cardiovascular disease (CVD) remains a leading cause of global mortality, accurate predictive models that enable timely diagnosis and intervention are critical. This paper provides an overview of a selection of ML models used for cardiac disease prediction through a comparative study concerning the performance, as well as explainability. We evaluate several algorithms LR, RF, SVM, and DT using a well-known dataset publicly available, which includes clinical measurements. The models are assessed using standard performance metrics. The deep learning models that exceed baseline models have achieved as accuracy (85%, 81%, 81%, 80%), sensitivity (recall) and specificity (80%, 80%, 80%, 77%), precision (92%, 86%, 86%, 85%), F1-Score (86%, 83%, 83%, 81%), MSE (0.15, 0.19, 0.19, 0.20), and ROC-AUC (90%, 87%, 89%, 80%) for LR, RF, SVM, and DT models, respectively. LR achieved the highest accuracy of 85% and ROC-AUC score of 90%, proving to be very efficient in predicting nonlinear patterns and reducing misclassification errors. These results will highlight the positive and negative aspects of each model, where Random Forest and Neural Networks are superior in terms of accuracy and ROC-AUC, whilst LR yields simplicity and interpretability. The work dissects the complex interplay of model complexity and interpretability and highlights which methods are best for predicting cardiac disease in clinical settings. The work will eventually transform how AI-enabled diagnostic devices evolve and set the bar for further research and real-world adoption in health matters. © 2025 IEEE.

Publication Title

2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2025

Publication Date

9-2025

ISBN

9798331532000

Keywords

AUC, clinical features, early diagnosis, healthcare, heart disease, predictive models

Repository Citation

Afroz, Sonia; Islam, Md Munjurul; Sharma, Nikunj; Rawat, Kanika; Saha, Tuhalika; Das, Rana; Mim, Sheikh Urvana Akter; Alam, Mst Sanjida; and Ali, Md. Akkas, "Predicting Heart Disease with Machine Learning: A Comparative Analysis of Multiple Models" (2025). Student Publications [Scholarly]. 96.
https://commons.clarku.edu/student_publications/96