Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier

Despite technological advancements, heart disease continues to be a major global health challenge, emphasizing the importance of developing accurate predictive models for early detection and timely intervention. This study proposes a heart disease prediction model integrating a stacking classifier w...

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Vydané v:Computers in biology and medicine Ročník 191; s. 110171
Hlavní autori: Sowmiya, M., Banu Rekha, B., Malar, E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.06.2025
Predmet:
Algorithms
Heart disease
Heart Diseases - diagnosis
Humans
Internal Medicine
Meta-heuristic feature selection
Models, Cardiovascular
Other
Predictive modelling
Salp swarm algorithm
Stacking classifier
Meta-heuristic feature selection
Salp swarm algorithm
Predictive modelling
Heart disease
Stacking classifier
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí:Despite technological advancements, heart disease continues to be a major global health challenge, emphasizing the importance of developing accurate predictive models for early detection and timely intervention. This study proposes a heart disease prediction model integrating a stacking classifier with a nature-inspired meta-heuristic algorithm. It employs an improved Binary Salp Swarm Algorithm (BSSA) by incorporating a wolf optimizer and opposition-based learning for optimal feature selection. The proposed Stacking Classifier (SC) architecture features a two-tier ensemble: heterogeneous base classifiers at level 0 and a meta-learner at level 1. The BSSA is used to identify optimal features, which are then utilized to construct the stacking classifier. Experimental results demonstrate superior performance, achieving 95 % accuracy, 0.92 sensitivity, 0.97 specificity, 0.96 precision, and an F1 score of 0.95, with notably low false positive and false negative rates. Further, validation on larger datasets yielded an accuracy of 87.46 %. The feature selection process adopts a multi-objective strategy which enhances the classification accuracy and outperforms conventional techniques. The proposed method demonstrates significant potential for improving the predictive modelling in clinical settings for diagnosing heart diseases. [Display omitted] •A novel automated model is proposed using clinical parameters and risk factors for early-stage heart disease detection.•A bio-inspired meta-heuristic Binary Salp Swarm Algorithm (BSSA) incorporating the Wolf Optimizer and opposition-based learning for optimal feature extraction.•An improved BSSA optimizes feature selection via follower updates guided by three leader salps: alpha, beta, and omega.•An Ensemble Stacking Classifier (SC) in the BSSA-SC framework boosts accuracy and outperforms state-of-the-art methods with low misclassification rates.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.110171