Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.
- 1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- 2Library, Wenzhou Medical University, Wenzhou, China. [email protected].
- 3The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. [email protected].
Abstract
Background
Cardiovascular 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.Methods
This 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.Results
QSAO-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.Conclusions
QSAO-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.
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BibTeX
@article{chen2026cardiovascular,
title = {{Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.}},
author = {Mengmeng Chen and Yani Yan and Yige Xue and Wenyi Tu},
journal = {BMC medical informatics and decision making},
year = {2026},
month = sep,
doi = {10.1186/s12911-026-03865-8},
url = {https://europepmc.org/article/MED/42902240},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Cardiovascular disease prediction and mortality risk assessment using a QSAO‑optimized BP neural network.
AU - Mengmeng Chen
AU - Yani Yan
AU - Yige Xue
AU - Wenyi Tu
PY - 2026
DA - 2026/09/01
JO - BMC medical informatics and decision making
DO - 10.1186/s12911-026-03865-8
UR - https://europepmc.org/article/MED/42902240
AB - Background Cardiovascular 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.Methods This 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.Results QSAO-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.Conclusions QSAO-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.
SN - 1472-6947
ER -