Heart Disease Prediction Using Logistics Regression

Authors: Murari Raja Krishna, Maddisetti Balu, Muppalla Ravi, Ms. Sivasangari

Abstract: Abstract- Cardiovascular disease remains the leading cause of global mortality, claiming approximately 17.9 million lives annually. Early and accurate prediction of individual patient risk is essential for enabling timely clinical intervention and reducing preventable mortality. This paper presents a comprehensive machine learning–based clinical decision support system for heart disease prediction using Logistic Regression, deployed as an interactive Streamlit web application. The system is trained and evaluated on the UCI Cleveland Heart Disease dataset comprising 303 patient records and 13 clinical attributes. The Logistic Regression model achieves a test accuracy of 88.52%, F1-score of 0.89, and AUC-ROC of 0.9267 on a held-out 20% test partition. Rigorous comparative evaluation against five baseline classifiers—Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest—confirms that Logistic Regression provides the best overall balance of interpretability, predictive performance, and generalization stability. The Streamlit web application enables real-time risk stratification and probabilistic output visualization across all 13 clinical features, bridging the gap between algorithmic performance and practical clinical utility.