Hybrid CA-SAE-AFB and ensemble learning framework for accurate heart disease prediction
Year: 2026
Type: article
Abstract: Heart disease prediction from structured clinical records requires models that can represent nonlinear feature interactions while avoiding optimistic estimates caused by data leakage and model-selection bias. This study presents a hybrid CA-SAE-AFB framework that combines a sparse autoencoder, adaptive latent-feature re-weighting, logistic regression, XGBoost, out-of-fold stacking, validation-only F1 threshold optimization, and a two-stage decision rule. The study includes a 50-seed stratified analysis for stability assessment and uses leakage-controlled nested stratified 5-fold outer cross-validation with a 3-fold inner loop as the primary evaluation protocol. All preprocessing, representation learning, threshold selection, and meta-learner fitting are restricted to the appropriate training/inner-validation partitions, while outer test folds remain untouched until final evaluation. Under the nested evaluation, CA-SAE-AFB achieved a mean accuracy of 0.8617 (95% CI: 0.8365–0.8869), F1-score of 0.8772 (95% CI: 0.8591–0.8952), MCC of 0.7208 (95% CI: 0.6695–0.7721), and ROC-AUC of 0.9153 (95% CI: 0.8944–0.9361). The repeated-split and nested-CV estimates were closely aligned. Component ablation showed the clearest directional contribution from adaptive feature boosting, whereas the effects of the sparse autoencoder and meta-learner were metric-dependent. On an NVIDIA Tesla T4 GPU, the complete single-sample inference pipeline required 83.18 ms on average over 200 timed runs after 50 warm-up runs. These results support computational feasibility and methodological robustness for further investigation in clinical decision-support settings, while external prospective clinical validation remains necessary.
Source: BMC Cardiovascular Disorders
Institution: SRM University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W7215700211
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