Piranha-Based feature subset selection and constructive sequential neural networks for enhanced cardiovascular disease prediction

Background Cardiovascular disease (CVD) is one of the most common causes of death globally. Early detection of CVD and timely treatment for preventing further health complications depend on several factors. Those variables include clinical data, redundant features, and existing machine learning models or deep learning models that do not achieve reliable prediction performance. Objective The primary objective of this research work is to develop an intelligent framework for predicting cardiovascular diseases that classifies the diseases accurately, reduces feature redundancy and enhances efficiency through optimizing feature selection. Methods The proposed Piranha-Based Feature Subset Selection with Constructive Sequential Neural Network (PFSS-CSNet) framework first applies Min-Max normalization to standardize the clinical data. The PFSS algorithm employs feature subset selection to identify the most informative clinical features. In addition, Bayesian optimization is used to determine the optimal hyperparameters of CSNet. The proposed framework is evaluated using the publicly available UCI Heart Disease dataset. Results The accuracy of the proposed PFSS-CSNet framework is 99.70%, the precision estimate is 99.50%, the recall is 99.80%, the specificity assessment is 99.60%, the F1-score is 99.60%, the G-measure is 99.70%, and the AUC is evaluated as 0.997. In comparative experiments, the proposed framework consistently outperforms existing machine learning classifiers in predicting cardiovascular diseases. Conclusion The incorporation of PFSS, Bayesian hyperparameter optimization, and CSNet provides a powerful and computationally efficient framework for predicting cardiovascular diseases. The proposed method achieves accurate prediction performance on the benchmark dataset and can aid clinical decision-making. Nonetheless, routine clinical adoption requires further verification using multi-center external clinical datasets.