The Heartcare: Predictive Analytics for Early Detection and Prevention

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, often due to late detection and prevention. Heartcare aims to leverage predictive analytics to facilitate early detection and prevention of heart diseases. By integrating machine learning algorithms such as Decision Trees, Random Forests, and Logistic Regression, Heartcare provides healthcare professionals with a powerful tool for patient health monitoring. This study focuses on developing a predictive model to assess heart disease risk using patient-specific data, such as age, sex, BMI, and lifestyle factors. The outcomes will enable healthcare professionals to make informed decisions, potentially saving lives and reducing healthcare costs.