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

AbstractThe 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.Keywords: Cardiovascular diseasefuzzy DEMATELCFCS methodentropy measurerule reductionMamdani fuzzy inference system Disclosure statementThe authors declare that they have no conflict of interest.Ethics statementThis article does not contain any studies with human participants or animals performed by any of the authors.Data availability statementOn reasonable request, the datasets used in the proposed investigation are available from the corresponding authorAdditional informationFundingThe authors have not received any funding.