{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:15:24Z","timestamp":1783149324662,"version":"3.54.6"},"reference-count":18,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100007414","name":"Qassim University","doi-asserted-by":"publisher","award":["QU-APC-2026"],"award-info":[{"award-number":["QU-APC-2026"]}],"id":[{"id":"10.13039\/501100007414","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate machine learning and deep learning models for the early prediction of dataset-defined myocardial infarction\/heart disease risk using structured demographic, clinical, electrocardiographic, exercise-testing, and angiographic-related variables. The study also aimed to improve the clinical interpretability and methodological transparency of artificial intelligence-based cardiovascular risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective computational modeling design was applied to a merged public heart disease dataset containing 1,888 records and 14 variables. The outcome was the original binary dataset label indicating higher versus lower likelihood of heart disease\/heart attack risk. Data preprocessing included missing-value management, categorical encoding, normalization of continuous variables, and stratified model evaluation. Three models were compared: Random Forest, Support Vector Machine, and a multilayer perceptron deep neural network. Model performance was assessed using accuracy, precision\/positive predictive value, recall\/sensitivity, F1-score, ROC-AUC, and class-wise support. A separate statistical analysis section was added to report descriptive analysis, outcome distribution, feature correlation, and clinically relevant evaluation metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Random Forest model achieved the strongest overall performance, with 97.0% accuracy, 96.0%-97.0% class-wise precision, 96.0%-97.0% class-wise recall, 97.0% F1-score, and ROC-AUC of 0.97. The deep neural network achieved 92.3% accuracy and ROC-AUC of 0.94, whereas the Support Vector Machine achieved 87.0% accuracy and ROC-AUC of 0.87. The balanced distribution of the dataset supported internal model training but may not reflect the lower prevalence of myocardial infarction in real-world healthcare settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings suggest that Random Forest can provide strong internal predictive performance for structured cardiovascular risk classification. However, the outcome represents a dataset-defined surrogate classification rather than independently adjudicated acute myocardial infarction. Therefore, external validation, calibration, comparison with validated clinical risk scores, and prospective evaluation in real-world clinical populations are required before clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261464747","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T11:23:54Z","timestamp":1782905034000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction of dataset-defined myocardial infarction risk: A retrospective computational study for precision cardiovascular risk assessment"],"prefix":"10.1177","volume":"12","author":[{"given":"Mohammad Subhi","family":"Al-Batah","sequence":"first","affiliation":[{"name":"Jadara University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alourani","sequence":"additional","affiliation":[{"name":"Qassim University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"crossref","unstructured":"Gangadhar MS Sai KVS Kumar SHS et al. Machine learning and deep learning techniques on accurate risk prediction of coronary heart disease. In: 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE 2023 pp. 227\u2013232.","DOI":"10.1109\/ICCMC56507.2023.10083756"},{"key":"e_1_3_8_3_2","doi-asserted-by":"crossref","unstructured":"Bakar WAWA Josdi NLNB Man MB et al. A review: Heart disease prediction in machine learning & deep learning. In: 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). IEEE 2023 pp. 150\u2013155.","DOI":"10.1109\/CSPA57446.2023.10087837"},{"key":"e_1_3_8_4_2","doi-asserted-by":"crossref","unstructured":"Mall S Singh J. Heart diagnosis using deep neural network. In: 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE 2023 pp. 7\u201312.","DOI":"10.1109\/ICCIKE58312.2023.10131696"},{"key":"e_1_3_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.008"},{"issue":"1","key":"e_1_3_8_6_2","first-page":"1","article-title":"Early detection of coronary heart disease based on machine learning methods","volume":"4","author":"Yilmaz R","year":"2022","unstructured":"Yilmaz R, Yagin FH. Early detection of coronary heart disease based on machine learning methods. Med Rec 2022; 4(1): 1\u20136. https:\/\/doi.org\/10.37990\/medr.1011924","journal-title":"Med Rec"},{"key":"e_1_3_8_7_2","first-page":"5080332","article-title":"Enhancing heart attack prediction with machine learning: A study at Jordan University Hospital","volume":"2024","author":"Alshraideh M","year":"2024","unstructured":"Alshraideh M, Alshraideh N, Alshraideh A, et al. Enhancing heart attack prediction with machine learning: A study at Jordan University Hospital. Appl Comput Intell Soft Comput 2024; 2024: 5080332. https:\/\/doi.org\/10.1155\/2024\/5080332","journal-title":"Appl Comput Intell Soft Comput"},{"key":"e_1_3_8_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8387680"},{"key":"e_1_3_8_9_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9580896"},{"key":"e_1_3_8_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14070736"},{"key":"e_1_3_8_11_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1022\/1\/012072"},{"key":"e_1_3_8_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-020-05105-1"},{"key":"e_1_3_8_13_2","doi-asserted-by":"publisher","DOI":"10.32996\/jcsts.2024.6.3.5"},{"key":"e_1_3_8_14_2","doi-asserted-by":"publisher","DOI":"10.32996\/bjns.2024.4.2.5"},{"key":"e_1_3_8_15_2","doi-asserted-by":"crossref","unstructured":"Miah J Ca DM Sayed MA et al. Improving cardiovascular disease prediction through comparative analysis of machine learning models: A case study on myocardial infarction. In: 2023 15th International Conference on Innovations in Information Technology (IIT). IEEE 2023 pp. 49\u201354.","DOI":"10.1109\/IIT59782.2023.10366476"},{"key":"e_1_3_8_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.04.011"},{"key":"e_1_3_8_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.105988"},{"issue":"2","key":"e_1_3_8_18_2","first-page":"2177","article-title":"Involving machine learning techniques in heart disease diagnosis: A performance analysis","volume":"13","author":"Shukur BS","year":"2023","unstructured":"Shukur BS, Mijwil MM. Involving machine learning techniques in heart disease diagnosis: A performance analysis. Int J Electr Comput Eng 2023; 13(2): 2177.","journal-title":"Int J Electr Comput Eng"},{"key":"e_1_3_8_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-13131-x"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261464747","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076261464747","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261464747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:18:11Z","timestamp":1783145891000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076261464747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":18,"alternative-id":["10.1177\/20552076261464747"],"URL":"https:\/\/doi.org\/10.1177\/20552076261464747","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"article-number":"20552076261464747"}}