{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T19:18:21Z","timestamp":1787685501804,"version":"build-2784847793"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","award":["MOH-000153"],"award-info":[{"award-number":["MOH-000153"]}],"id":[{"id":"10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","award":["MOH-000762"],"award-info":[{"award-number":["MOH-000762"]}],"id":[{"id":"10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","award":["MOH-001193)"],"award-info":[{"award-number":["MOH-001193)"]}],"id":[{"id":"10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Leong Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Owing to the breadth of complex and highly dimensional clinical data associated with ageing, integration of multiple health domains is needed towards determining cardiac outcomes of older adults. We designed a machine learning (ML) approach to conglomerate multi-domain data and identify determinants of cardiac function in older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>We applied a structured ML pipeline including data pre-processing, feature selection, and model development using Random Forest, Gradient Boosting, XGBoost, LightGBM, and support vector machine. Model performance was evaluated using stratified k-fold cross-validation and complementary discrimination metrics, including ROC\u2013AUC, PR-AUC, balanced accuracy, sensitivity, and specificity. Feature importance was assessed using Random Forest (RF) importance and Shapley Additive exPlanations (SHAP), and the Tree-based Pipeline Optimization Tool (TPOT) was used for model optimization. The outcome was an impaired myocardial relaxation phenotype based on the mitral peak early-to-late diastolic filling velocity (E\/A) ratio. The multi-domain dataset included demographic characteristics, clinical risk factors, physical activity, body composition, serum biomarkers, omics, and cardiac imaging, comprising 227 features from 984 older adults. Thirty key features were identified, mainly related to physical function and metabolomics. Using these features, the selected classifiers achieved ROC\u2013AUC values above 0.79. XGBoost was retained as the primary tree-ensemble benchmark, with cross-validated ROC\u2013AUC 0.8157 and test-set ROC\u2013AUC 0.7658; TPOT was comparable (test-set ROC\u2013AUC 0.7675). Higher XGBoost score was associated with death-or-admission events (HR 1.115, P = 0.029).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multi-domain ML identified clinically interpretable signals associated with impaired myocardial relaxation in ageing and with clinical events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>ClinicalTrials.gov Identifier: NCT02791139.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag136","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:15:48Z","timestamp":1786706148000},"source":"Crossref","is-referenced-by-count":0,"title":["A machine learning approach to conglomerate multi-domain features of cardiac 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University of Technology and Design , 8 Somapah Road, Singapore 487372 ,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hane","family":"Naghshbandi","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University , 11 Mandalay Road, Singapore 308232 ,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8176-0498","authenticated-orcid":false,"given":"Johnathan","family":"Loh","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National Heart Centre Singapore , 5 Hospital Drive, Singapore 169609 ,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinliu","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National University of Singapore , 4 Engineering Drive 3, Singapore 117583 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Cardiovascular & Metabolic Disorders, Duke-National University of Singapore Medical School , 8 College Road, Singapore 169857 ,","place":["Singapore"]},{"name":"Department of Endocrinology, Singapore General Hospital , 20 College Road, Singapore 169856 ,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2086-6517","authenticated-orcid":false,"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National Heart Centre Singapore , 5 Hospital Drive, Singapore 169609 ,","place":["Singapore"]},{"name":"SingHealth Duke-NUS Cardiovascular Sciences Academic Clinical Programme, Duke-NUS Medical School , 8 College Road, Singapore 169857 ,","place":["Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-6019","authenticated-orcid":false,"given":"Si Yong","family":"Yeo","sequence":"additional","affiliation":[{"name":"Lee 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