Optimizing stability of heart disease prediction across imbalanced learning with interpretable Grow Network

Background and objectives: Heart disease prediction models often face stability challenges when applied to public datasets due to significant class imbalances, unlike the more balanced benchmark datasets. These imbalances can adversely affect various stages of prediction, including feature selection, sampling, and modeling, leading to skewed performance, with one class often being favored over another. Methods: To enhance stability, this study proposes a Grow Network (GrowNet) architecture, which dynamically configures itself based on the data's characteristics. To enhance GrowNet's stability, this study proposes the use of TriDyn Dependence feature selection and Adaptive Refinement sampling, which ensure the selection of relevant features across imbalanced data and effectively manage class imbalance during training. Results: When evaluated on the benchmark UCI heart disease dataset, GrowNet has outperformed other models, achieving a specificity of 92%, sensitivity of 88%, precision of 90%, and F1 score of 90%. Further evaluation on three public datasets from the Behavioral Risk Factor Surveillance System (BRFSS), where heart disease cases constitute only about 6% of the data, has demonstrated GrowNet's ability to maintain balanced performance, with an average specificity, sensitivity, and AUC-ROC of 77.67%, 81.67%, and 89.67%, respectively, while other models have exhibited instability. This represents a 22.8% improvement in handling class imbalance compared to prior studies. Additional tests on two public datasets from the National Health Interview Survey (NHIS) have confirmed GrowNet's robustness and generalizability, with an average specificity, sensitivity, and AUC-ROC of 80.5%, 82.5%, and 90%, respectively, while other models have continued to demonstrate instability. Discussion: To enhance transparency, this study incorporates SHapley Additive exPlanations (SHAP) analysis, enabling healthcare professionals to understand the decision-making process and identify key risk factors for heart disease, such as bronchitis in midlife, renal dysfunction in the elderly, and depressive disorders in individuals aged 35-44. Conclusion: This study presents a robust, interpretable model to assist healthcare professionals in cost-effective, early heart disease detection by focusing on key risk factors, ultimately improving patient outcomes.