Heart Disease Prediction (KEY IJP************749)

• Sanskriti Balapurkar

Abstract

The World Health Organization reports that cardiovascular diseases, particularly heart disease, heart disease kills 17 million people every year, making it the number one reason people die everywhere in the world. This study introduces an innovative machine learning framework designed to predict heart disease susceptibility by analyzing comprehensive patient medical records and risk factors. By amalgamating a suite of advanced algorithms including decision trees, random forest, logistic regression our model systematically scrutinizes a diverse dataset to unveil patterns indicative of heart disease. The results suggest our approach holds promise as a potent tool for early identification of heart disease, potentially leading to improved patient outcomes through timely intervention and treatment.