MACHINE LEARNING HEART DISEASE PREDICTION WITH EFFECTIVE FEATURE ENGINEERING

Abstract

Early diagnosis is vital for heart 
disease, which influences millions around the world. 
Principal Component analysis is utilized to find and 
work on the main qualities in an original element 
designing technique. The examination utilizes AI to 
immediately figure Heart Disease condition and make 
a move. Incorporate undertaking utilizes a Stacking 
Classifier to coordinate RF, MLP, and LightGBM 
expectations. This methodology synergistically 
utilizes model qualities to make a powerful and exact 
figure with 100 percent accuracy. PCHF-based 
highlights were utilized to produce the model, and the 
Stacking Classifier was prepared for front-end 
arrangement. Flask framework with client validation 
makes client testing easy and safe, advancing the ML
based heart disease prediction system.