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Fuzzy entropy DEMATEL inference system for accurate and efficient cardiovascular disease diagnosis

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  • Stephen Mariadoss
  • Felix Augustin

Abstract

The global population is at risk from both communicable and non-communicable deadly diseases, including cardiovascular disease. Early detection and prevention of cardiovascular disease require an accurate self-detection model. Therefore, this study introduces a novel fuzzy entropy DEMATEL inference system for accurate self-detection of cardiovascular disease. It combines fuzzy DEMATEL, entropy, and Mamdani fuzzy inference, utilizing innovative strategies like attribute reduction, entropy-based clustering, influential factor selection, and rule reduction. The system achieves high accuracy (98.69%) and sensitivity (98.62%), outperforming existing methods. Validation includes satisfactory factor analysis, performance measures and statistical analysis, demonstrating its effectiveness in addressing complexity and prioritizing factors.

Suggested Citation

  • Stephen Mariadoss & Felix Augustin, 2024. "Fuzzy entropy DEMATEL inference system for accurate and efficient cardiovascular disease diagnosis," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(11), pages 1460-1491, August.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:11:p:1460-1491
    DOI: 10.1080/10255842.2023.2245518
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