Classification model for heart disease prediction using correlation and feature selection techniques

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

“…While computationally efficient, filter methods may overlook feature interactions. 16 , 17 , 18 , 19 Wrapper methods, on the other hand, evaluate feature subsets by training and evaluating models with different feature combinations, employing search strategies like forward selection or backward elimination to find the optimal subset. While effective, wrapper methods can be computationally expensive due to repeated model training.…”