Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.

backgroundCardiovascular disease remains a leading cause of mortality worldwide, imposing a substantial burden on global healthcare systems. Early detection and accurate mortality risk assessment are critical for timely intervention and improved patient outcomes. However, existing predictive models often suffer from limited clinical applicability due to their restriction to single diagnostic tasks, insufficient prediction accuracy, and a tendency toward overfitting.

methodsThis study proposes a novel hybrid intelligent model, termed QSAO-BP, which integrates a quartile based Snow Ablation Optimizer (QSAO) with a Back Propagation (BP) neural network for simultaneous cardiovascular disease (CVD) diagnosis and mortality risk assessment. The QSAO enhances the SAO by incorporating Gaussian and Lévy random walks through a quartile based strategy, effectively mitigating premature convergence and local optima entrapment of SAO. The QSAO optimizes the hyperparameters of the BP neural network to improve its predictive accuracy and generalization capability. A dual validation framework is established using the Cleveland Heart Disease Dataset and the Heart Failure Clinical Records Dataset. Extensive experiments compare QSAO-BP against nine optimizer based BP variants and four classical machine learning algorithms across ten evaluation metrics.

resultsQSAO-BP achieves superior performance on both datasets, attaining an accuracy of 0.873 on Dataset 1 and 0.854 on Dataset 2, representing improvements of 8.178% and 9.347% over SAO-BP, respectively. The model also achieves the highest accuracy, precision, recall, F1-score, and MCC while maintaining competitive specificity and NPV. SHAP analysis reveals that the key predictors identified by QSAO-BP are consistent with established clinical knowledge.

conclusionsQSAO-BP delivers robust and accurate predictions across two distinct clinical tasks with satisfactory interpretability, positioning it as a promising tool to support clinical decision making in CVD.