A Stacked Meta Neural Network with Adaptive Nonlinear Decision Fusion for Cardiovascular Disease Prediction

Abstract

Cardiovascular disease (CVD) remains a leading global cause of mortality, emphasizing the need for reliable early prediction systems. This study proposes a Stacked Meta Neural Network (SMNN) that integrates multiple machine learning classifers through nonlinear decision fusion. In the frst stage, six base models generate probabilistic outputs using a k-fold out-of-fold (OOF) strategy. These are then combined by a shallow Artifcial Neural Network (ANN) meta-learner to capture hidden nonlinear interactions. Experimental evaluation on a dataset of over 66,000 records achieved strong performance, with high recall and balanced ROCAUC, demonstrating the SMNN’s efectiveness as a robust and generalizable tool for CVD risk prediction.