Optimizing predictive performance in heart disease diagnosis with stacked wrapper pre-processing techniques.

Abstract

Heart disease prediction utilizes machine learning methods to estimate the probability of a person developing heart disease, considering various medical and lifestyle factors. These factors commonly include age, gender, cholesterol levels, blood pressure, smoking habits, diabetes status, family history, and physical activity. In real-world medical datasets, patient information may be missing or incomplete, which can hinder accurate predictions. Heart disease datasets often have more negative samples (people without heart disease) than positive ones. This imbalance can lead to biased models that predict the majority class (no heart disease) more accurately. Hence, this paper presents the Stacked Wrapper Attribute Machine Learning Model (SWA-ML) for the prediction and classification of heart disease. The proposed SWA-ML model incorporates the multi-class estimation of features in the heart disease dataset. The SWA-ML utilizes the stacked wrapper model for the estimation of features and classification. The machine learning model integrates the estimated features for classification. The model integrates three distinct datasets, resulting in a combined dataset of 1800 instances, encompassing key attributes such as age, sex, cholesterol levels, and blood pressure. Wrapper-based feature selection and stacking ensemble architecture are used in the SWA-ML framework to forecast heart disease utilising heterogeneous tabular clinical datasets. After attribute alignment, duplication removal, missing-value treatment, categorical encoding, feature scaling, and class balancing, three public heart disease datasets were integrated. Before stacked ensemble learning, wrapper-based feature selection approaches such RFE, Forward Selection, Backward Elimination, and Genetic Algorithm optimisation were compared. For robust model assessment, stratified train-test partitioning, k-fold cross-validation, and hyperparameter optimisation were used. Classification accuracy, precision, recall, F1-score, and AUC were consistently higher for the stacked wrapper configuration than for individual feature-selection procedures. Ablation experiments, statistical significance testing, calibration evaluation, and comparing with existing methodologies confirm the framework's efficacy. SWA-ML appears to be an effective and interpretable decision-support mechanism for structured clinical data-based heart disease risk prediction.