A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridKadry, Seifedine/0000-0002-1939-4842
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidKadry, Seifedine/C-7437-2011
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorGuldogan, Emek
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorPinar, Abdulvahap
dc.contributor.authorColak, Cemil
dc.contributor.authorKadry, Seifedine
dc.contributor.authorKim, Jungeun
dc.date.accessioned2024-08-04T20:54:56Z
dc.date.available2024-08-04T20:54:56Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractCardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.en_US
dc.description.sponsorshipBasic Science Research Program [2020R1I1A3069700]; Technology Development Program of MSS [S3033853]en_US
dc.description.sponsorshipThis research was partly supported by Basic Science Research Program (No.2020R1I1A3069700) and by the Technology Development Program of MSS (No.S3033853).en_US
dc.identifier.doi10.1038/s41598-023-49673-2
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid38092844en_US
dc.identifier.scopus2-s2.0-85179667134en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-023-49673-2
dc.identifier.urihttps://hdl.handle.net/11616/101724
dc.identifier.volume13en_US
dc.identifier.wosWOS:001126324100005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWomenen_US
dc.subjectModelen_US
dc.subjectGisen_US
dc.titleA proposed tree-based explainable artificial intelligence approach for the prediction of angina pectorisen_US
dc.typeArticleen_US

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