Heart Disease Prediction Using Machine Learning Algorithms

Abstract: Heart disease remains a pervasive global health challenge, demanding innovative approaches for prediction and management. In this study, we investigate the efficacy of three distinct machine learning algorithms – logistic regression, knearest neighbors (KNN), and random forest classifier – for heart disease prediction using comprehensive clinical datasets. Through a rigorous evaluation of model performance and feature importance analysis, our research sheds light on the potential of machine learning techniques to augment traditional risk assessment methods. Notably, our study delves into the interpretability of models, offering insights into the underlying factors influencing heart disease prediction. By elucidating the strengths and limitations of each algorithm, we aim to empower healthcare practitioners with enhanced decision support tools for early intervention and personalized treatment strategies. This research represents a pivotal step forward in the integration of advanced computational methodologies into cardiovascular care, with profound implications for improving patient outcomes and healthcare delivery systems.