MEDPROGNOSIS: A HEART DISEASE FORECASTING SOLUTION

Abstract

Cardiovascular diseases (CVDs) are still one of the leading causes of illness worldwide. Identifying heart diseases early and accurately predicting them can play a critical role in preventive healthcare.

This research explores several machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, and Decision Tree, to proactively identify people undergoing medical treatment who may be at risk of heart irregularities.  The study highlights the exceptional performance of random forest in handling complex datasets.he primary goal of this research is to create a resilient predictive model based on relevant parameters that can identify potential cardiac issues in a timely and accurate manner. Compared to traditional models, our findings show a significant improvement in prediction accuracy. A supervised learning approach can effectively capture complex relationships between genetic factors and clinical parameters, which can aid in better understanding individualized cardiovascular risk.

These findings suggest that a data-driven and personalized approach can help with early detection and targeted intervention, ultimately improving the effectiveness of preventive healthcare strategies in combating cardiovascular diseases.