A Coherent LPB-Family Optimization Framework for Multilayer Perceptron Training in Heart Disease Prediction
DOI:
https://doi.org/10.25195/ijci.v52i1.711Keywords:
Machine learning, Multilayer Perceptron (MLP), heart disease prediction, neural network training, LPB-family optimization, mLPB-MLP.Abstract
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 Bayesian
Hyperparameter 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 schema
alignment, 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.
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