{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:00:36Z","timestamp":1791187236393,"version":"4.1.0"},"reference-count":0,"publisher":"Maad Rayan Publishing Company","issue":"3","content-domain":{"domain":["jcvtr.tbzmed.ac.ir"],"crossmark-restriction":false},"short-container-title":["J Cardiovasc Thorac Res"],"published-print":{"date-parts":[[2025,9,28]]},"abstract":"<jats:p>Introduction: The interplay between coronary disease and neurocognitive dysfunction remains unclear with several underlying factors likely contributing to this complex relationship. This study develops a predictive model using a machine learning approach to determine a predictive model of neurocognitive functioning in patients with acute coronary syndrome (ACS). Methods: Sixty-three patients, enrolled in the phase III cardiac rehabilitation program, underwent a neurocognitive assessment. To predict neurocognitive functioning a cross validated random forest model was used (RF_cv) due to its robustness to non-linear relationships and overfitting, and its successful application in prior disease prediction studies. Results: The RF_cv model showed an r-squared of 0.978, an RMSE of 0.6309 and a MAE value of 0.479. The top-ten predictors in the model were: HDL, Depression, Glucose, Glycated Hemoglobin, B-Type Natriuretic Peptide, BMI (Kg\/m2), Waist-to-Hip Ratio, Cholesterol, Anxiety and Age. Conclusion: The variance in neurocognitive functioning is explained by a combination of biochemical indicators and body composition, reflecting classical cardiovascular risk factors and depression. The obtained RF-cv predictive model supports early identification of patients for tailored interventions. <\/jats:p>","DOI":"10.34172\/jcvtr.025.33340","type":"journal-article","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T11:23:40Z","timestamp":1760181820000},"page":"181-187","update-policy":"https:\/\/doi.org\/10.34172\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predictive model of neurocognitive functioning after acute coronary syndrome. A machine learning approach"],"prefix":"10.34172","volume":"17","author":[{"given":"In\u00eas","family":"Moreira","sequence":"first","affiliation":[{"name":"Department of Social and Behavioral Sciences of University Institute of Health Sciences, Gandra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Peixoto","sequence":"additional","affiliation":[{"name":"Psychosocial Rehabilitation Laboratory, Rehabilitation Investigation Center, School of Health, Polytechnic University of Porto, Gandra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dulce","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Psychology, Unidade Local de Sa\u00fade de S\u00e3o Jo\u00e3o, Gandra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afonso","family":"Rocha","sequence":"additional","affiliation":[{"name":"Cardiocare Research Group, Faculty of Medicine, University of Porto, Gandra, Portugal"},{"name":"Department of Physical and Rehabilitation Medicine, Unidade Local de Sa\u00fade de S\u00e3o Jo\u00e3o, Gandra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-6330","authenticated-orcid":true,"given":"Bruno","family":"Peixoto","sequence":"additional","affiliation":[{"name":"Department of Social and Behavioral Sciences of University Institute of Health Sciences, Gandra, Portugal"},{"name":"Associate Laboratory i4HB - Institute for Health and Bioeconomy, University Institute of Health Sciences \u2013 CESPU, Gandra, Portugal"},{"name":"UCIBIO - Applied Molecular Biosciences Unit, Translational Toxicology Research Laboratory, University Institute of Health Sciences , Gandra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"20123","published-online":{"date-parts":[[2025,9,28]]},"container-title":["Journal of Cardiovascular and Thoracic Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jcvtr.tbzmed.ac.ir\/PDF\/jcvtr-17-181.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jcvtr.tbzmed.ac.ir\/PDF\/jcvtr-17-181.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T11:23:40Z","timestamp":1760181820000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcvtr.tbzmed.ac.ir\/Article\/jcvtr-33340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,28]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9,28]]},"published-print":{"date-parts":[[2025,9,28]]}},"URL":"https:\/\/doi.org\/10.34172\/jcvtr.025.33340","relation":{},"ISSN":["2008-5117","2008-6830"],"issn-type":[{"value":"2008-5117","type":"print"},{"value":"2008-6830","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,28]]},"assertion":[{"value":"Tabriz University of Medical Sciences","name":"journal_owner","label":"Journal Owner"},{"value":"Tabriz University of Medical Sciences","name":"journal_publisher","label":"Journal Publisher"},{"value":"2024-08-24","order":1,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}