Exploring Predictive factors for Heart Disease: A Comprehensive Analysis Using Logistic Regression

This study investigates the predictive factors for heart disease by applying logistic regression analysis on a dataset containing various health indicators. By using a backward elimination approach and Akaike Information Criterion (AIC) for model selection, significant predictors such as sex, chest pain type, blood pressure, cholesterol level were identified. Assumption checks confirmed the model’s validity. Visualizations and statistical summaries provided insights into the relationships between heart disease occurrence and key predictors. The final logistic regression model, validated by ROC curve and a confusion matrix, demonstrates a robust predictive capability. The primary contribution of this study lies in demonstrating logistic regression techniques and model validation through tools like ROC curves and confusion matrices. This research thus serves as a tutorial on statistical modeling of heart disease risk factors.