Comparative Analysis of Classification Models for Heart Disease Prediction Using Machine Learning

Among the most common health problems of modernity is heart disease, where early diagnosis becomes crucial for effective treatment and prevention. One cardinal responsibility of health professionals is the identification of at-risk persons to help decrease mortality through timely interventions. In this regard, a comparative analysis of machine learning classification models for heart disease prediction using a dataset with 13 medically relevant features such as gender, blood pressure, cholesterol level, family history, smoking habits, diet, treatment history, obesity, hypertension, and physical inactivity can be found in this study. The main objective of this research work is to find the most accurate and efficient classification model to predict the heart disease infallibly. Machine learning is a significant contribution to healthcare because it provides vast amounts of medical data that may be practically analyzed to derive actionable insights for clinical decision-making. Support Vector Machine (SVM) and Random Forest are the special focus among all examined models. It is found from the results that both models are doing very well in terms of predicting heart disease but Random Forest proved itself as a superior model as it achieved the highest accuracy in this study.