Heart Disease Detection and Classification Using Machine Learning
Vishwajeet Vinod Tayde, Komal Bhimrao Jadhal, Rushali Govardhan Lanjulkar, Radha Vitthal Bakal, Santosh Shriram Mhaske
Cardiovascular disease, Machine learning, Random Forest, GUI-based model, Prediction, Underserved communities
Cardiovascular diseases (CVDs) pose a substantial health burden in India, especially in regions with inadequate medical resources. This study aims to identify predictive parameters for CVD detection, specifically focusing on factors accessible in low-resource settings. We present a GUI-based heart disease prediction model utilizing the Random Forest algorithm, offering a user-friendly solution for early detection of CVDs in underserved communities. Through machine learning techniques, our model facilitates efficient risk assessment, contributing to improved healthcare outcomes in areas with limited access to medical facilities.
Article Details
Unique Paper ID: 163844

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2513 - 2518
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