HEART DISEASE PREDICTION USING LOGISTIC REGRESSION

Abstract: This study uses a carefully chosen patient dataset that includes a variety of demographic traits, lifestyle factors, and medical histories to reliably predict heart illness using logistic regression. A representative portion of the information is used to train the model (Logistic Regression), which was selected due to its efficacy in binary classification, to find intricate patterns that may indicate the risk of heart illness. A comprehensive health profile that includes lifestyle variables, physiological markers, and patient demographics allows for a more nuanced risk assessment. Extensive testing on an independent sample confirms the model's excellent discrimination accuracy between those with and without heart disease. This study advances data-driven healthcare by demonstrating how Logistic Regression might improve the precision of heart disease prediction. The findings have implications for proactive cardiovascular health management and individualized patient care through educated clinical decision-making.

Keywords: F1 score, Heart Disease, Logistic Regression, Precision, Recall, Sensitivity Analysis, Variable Selection.