{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:59:29Z","timestamp":1775199569048,"version":"3.50.1"},"reference-count":47,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. 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The aim was to enhance both predictive accuracy and model transparency, empowering clinicians to comprehend and trust the model\u2019s decisions. The methodology included data preprocessing, designing a CNN architecture tailored for tabular data, and integrating SHAP. The results showed superior predictive performance compared with the baseline models, with 98.54% accuracy, 97.14% sensitivity, 100% specificity, and SHAP, providing valuable insights into feature importance. This research advances heart disease prediction by harnessing the adaptability of CNNs to structured tabular datasets, while addressing the critical need for model interpretability in healthcare applications.<\/jats:p>","DOI":"10.1088\/2632-2153\/adfd39","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T22:54:58Z","timestamp":1755644098000},"page":"035043","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Heart disease prediction by tabular modeling with deep learning network and interpretability"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4151-5708","authenticated-orcid":true,"given":"Mohammad H","family":"Alshayeji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1849-9316","authenticated-orcid":true,"given":"Sa\u2019ed","family":"Abed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"mlstadfd39bib1","article-title":"World Health Organization, Cardiovascular diseases","author":"Cardiovascular diseases"},{"key":"mlstadfd39bib2","article-title":"Heart disease \u2013 Symptoms and causes","author":"Mayo Clinic,"},{"key":"mlstadfd39bib3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0248240","article-title":"The impact of heart failure on patients and caregivers: a qualitative study","volume":"16","author":"McHorney","year":"2021","journal-title":"PLoS One"},{"key":"mlstadfd39bib4","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.46006","article-title":"Cardiac rehabilitation in the modern era: optimizing recovery and reducing recurrence","volume":"15","author":"Zaree","year":"2023","journal-title":"Cureus"},{"key":"mlstadfd39bib5","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.50644","article-title":"Updates in the management of coronary artery disease: a review article","volume":"15","author":"Bansal","year":"2023","journal-title":"Cureus"},{"key":"mlstadfd39bib6","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/a17020078","article-title":"A review of machine learning\u2019s role in cardiovascular disease prediction: recent advances and future challenges","volume":"17","author":"Naser","year":"2024","journal-title":"Algorithms"},{"key":"mlstadfd39bib7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-12497-7","article-title":"Leveraging clinical data across healthcare institutions for continual learning of predictive risk models","volume":"12","author":"Amrollahi","year":"2022","journal-title":"Sci. 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