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Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

Computers in biology and medicine, 2024-05, Vol.173, p.108345-108345, Article 108345 [Peer Reviewed Journal]

2024 Elsevier Ltd ;Copyright © 2024 Elsevier Ltd. All rights reserved. ;2024. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2024.108345 ;PMID: 38564852

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  • Title:
    Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation
  • Author: Hosseini Chagahi, Mehdi ; Mohammadi Dashtaki, Saeed ; Moshiri, Behzad ; Jalil Piran, M.d.
  • Subjects: Accuracy ; Aggregation ; Aggregation layer ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - diagnosis ; Classification ; Classifier selection ; Classifiers ; Dependent ordered weighted averaging (DOWA) operator ; Disease detection ; Disease recognition ; Feature transformation ; Global health ; Humans ; Machine Learning ; Preventable deaths ; Public health ; Quality of Life ; Reproducibility of Results ; ROC Curve ; Stack-based ensemble classifier ; Support vector machines
  • Is Part Of: Computers in biology and medicine, 2024-05, Vol.173, p.108345-108345, Article 108345
  • Description: Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier’s reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases. •Feature transformation techniques enhance feature representation and quality.•Adding an aggregation layer to the stacking classifier improved the results.•Fine-tuning of hyper-parameters led to a significant improvement in results.•Different OWA operators are suitable candidates for implementing the aggregation layer.•Combining base classifiers can improve performance.
  • Publisher: United States: Elsevier Ltd
  • Language: English
  • Identifier: ISSN: 0010-4825
    EISSN: 1879-0534
    DOI: 10.1016/j.compbiomed.2024.108345
    PMID: 38564852
  • Source: MEDLINE
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