A Coherent LPB-Family Optimization Framework for Multilayer Perceptron Training in Heart Disease Prediction

Cardiovascular diseases (CVDs) are the leading cause of mortality in the world. This paper constructs models of heart disease classification using a single-hidden-layer Multilayer Perceptron (1HL-MLP) that is trained using a bi-level model that utilizes BayesianHyperparameter Optimization (HPO) and three variations of evolutionary strategy-based Learner Performance-Based Behavior (LPB): LPB-MLP, aLPB-MLP and mLPB-MLP. Each of the four heart disease datasets was processed in a common pipeline based on schemaalignment, median imputation, one-hot encoding, per-fold Z-score normalization, and Outof-Fold (OOF) threshold tuning and their performance was checked by stratified K-fold and external testing on an independent dataset. Results have shown that the Modified LPB model (mLPB-MLP) performed better, and it has the highest discrimination and calibration (AUC = 0.9782, AUPRC = 0.9732) and the overall accuracy (93.66%), F1-score (93.53%), recall (94%), specificity (93%), and the lowest BCE loss (0.193). These findings indicate consistent optimization processes, reasonable probability tuning and sensitivity-specificity compromise.In general, the Bayesian HPO in combination with LPB-family evolutionary training using data will lead to a clinically robust, well-calibrated, and reproducible heart disease risk prediction model.