Leveraging Machine Learning Techniques to Predict Cardiovascular Heart Disease

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dc.contributor.authorBasar, Remzi
dc.contributor.authorOcak, Oznur
dc.contributor.authorErturk, Alper
dc.contributor.authorde la Roche, Marcelle
dc.date.accessioned2025-10-11T20:47:47Z
dc.date.available2025-10-11T20:47:47Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractCardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study's limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection.en_US
dc.identifier.doi10.3390/info16080639
dc.identifier.issn2078-2489
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-105014328245en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.3390/info16080639
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21565
dc.identifier.volume16en_US
dc.identifier.wosWOS:001557688000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofInformationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectheart disease predictionen_US
dc.subjectartificial neural network (ANN)en_US
dc.subjectmachine learningen_US
dc.subjectSHAPen_US
dc.subjectmedical diagnosticsen_US
dc.subjectensemble learningen_US
dc.subjectclass imbalanceen_US
dc.titleLeveraging Machine Learning Techniques to Predict Cardiovascular Heart Diseaseen_US
dc.typeArticleen_US

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