Modular Architecture for Heart Risk Detection via Predictive Modeling

Abstract

This study details the creation of a machine learning- based framework for forecasting the onset of heart disease. Leveraging classifiers such as Logistic Regression, Decision Trees, and Random Forests, our system processes patient metrics to evaluate cardiovascular risk. Incorporating an intuitive interface and comprehensive evaluation measures, the tool aids clinicians by delivering accurate, timely assessments grounded in clinical variables.