Exploring Heart Disease Prediction through Machine Learning Techniques

Currently, heart disease stands as the most formidable threat to human life. The application of machine learning in scrutinizing data holds the promise of augmenting early detection and prevention strategies for this ailment. Within this study, a suite of six distinctive and classical machine learning models-Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Classifier, and Neural Network-are introduced and meticulously evaluated. These models leverage data gathered from heart patients across four distinct regions, contributing to a comprehensive assessment. The investigative process encompasses five pivotal stages: data collection, preprocessing, K-Means clustering, application of classification algorithms models for heart disease prediction, and rigorous evaluation. At the end of the study, a comprehensive summary of heart disease prediction was presented.