Classification of Heart Diseases Based on Machine Learning: A Review

Adnan Mohsin Abdulazeez (Duhok Polytechnic University, Duhok, Iraq)
Shereen Sadiq Hasan (Duhok Polytechnic University, Duhok, Iraq)

International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025

This review emphasizes the critical need for the early and accurate diagnosis of cardiovascular diseases (CVDs), a leading global cause of mortality. Recent advancements in machine learning (ML) offer promising solutions for classifying cardiac disorders and enhancing healthcare practices. This article discusses the benefits and limitations of current ML algorithms, evaluating supervised learning techniques (e.g., support vector machines, decision trees, and neural networks) and unsupervised methods (e.g., clustering algorithms) in identifying significant patterns within diverse datasets. Furthermore, it explores ensemble approaches and deep learning to improve classification accuracy. Ultimately, this study highlights ML's transformative potential in advancing heart disease diagnosis, improving patient outcomes, and reducing healthcare costs.