Performance Comparison of Machine Learning Models for Heart Disease Prediction

Author(s)	Dr. Vijay Kumar Samyal, Mr. Satyam Singh
Country	India
Abstract	Heart-related diseases, also known as Cardiovascular Diseases (CVDs), are a major cause of death worldwide, and early prediction is essential for timely treatment. Machine learning techniques are increasingly used to analyze medical datasets and support clinical decision-making. In this study, three machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), and Random Forest—are applied to the UCI Heart Disease dataset, which contains 14 clinical features. The dataset is split into an 80:20 ratio for training and testing, and feature scaling is performed where required. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The results show that the Random Forest model achieves the highest performance due to its ability to capture non-linear patterns, while SVM also performs well, and Logistic Regression serves as a strong baseline. The findings highlight that machine learning models, especially ensemble methods, can effectively assist in early heart disease prediction.
Keywords	Health Disease Prediction, Machine Learning, Medical Diagnosis, Clinical Data Analysis
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-12

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