A Study on Heart Disease Prediction Using Machine Learning Algorithms

Abstract

The incidence of heart disease is rapidly increasing, underscoring the critical need to proactively identify potential illnesses. This diagnostic task is intricate, demanding precision and efficiency. The central focus of the research paper lies in discerning, based on diverse medical characteristics, which patients are more predisposed to heart disease. By leveraging the patient's medical history, we devised a method to assess the likelihood of a heart disease diagnosis. Employing a range of machine learning algorithms such as KNN and logistic regression, we aimed to predict and classify patients at risk of heart disease. The study also explores how the model's application can enhance the accuracy of predicting heart attacks.