{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:25:30Z","timestamp":1792092330606,"version":"4.4.0"},"reference-count":22,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Hyperhomocysteinemia (HHcy) is recognized as an independent risk factor for coronary heart disease (CHD), yet accurately predicting CHD risk in patients with HHcy remains a challenge. This study aimed to develop and validate multiple machine learning models for predicting CHD risk in patients with HHcy and elucidate key predictors using Shapley Additive Explanation (SHAP) algorithms.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to develop and validate machine learning models for predicting the risk of coronary heart disease in individuals with normal homocysteine levels, aiming to improve early risk stratification and clinical decision-making.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n This single-center retrospective study collected data from patients who were diagnosed with HHcy through electronic medical records, which were randomly divided into training (n=364, 70%), validation (n=78, 15%), and test (n=78, 15%) sets. Seven machine learning models were constructed, including logistic regression, k-nearest neighbor, decision tree, random forest, extreme gradient boost, light gradient boosting machine (LightGBM), and stacking. Six core variables (age, weight, hypertension, continuous drinking history, activated partial thromboplastin time, and carotid plaque) were utilized as inputs, with performance evaluation metrics encompassing area under the receiver operating characteristic curve, accuracy,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score, calibration curve, Brier score, and decision curve analysis. Additionally, SHAP interpretation was conducted on the optimal LightGBM model.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The LightGBM model exhibited superior performance in the test set (area under the receiver operating characteristic curve=0.807,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=0.606), demonstrated good calibration (Brier score=0.2415), and yielded high clinical net benefit. SHAP analysis revealed age and activated partial thromboplastin time as the most influential predictors, followed by hypertension, weight, carotid plaque, and continuous drinking history. The correlation heat map illustrated low collinearity among variables, ensuring model stability.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The LightGBM model demonstrated high accuracy and interpretability in forecasting CHD risk among patients with HHcy. The integration of machine learning and interpretable artificial intelligence methods holds promise for delivering personalized early risk assessment and intervention strategies in clinical settings.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80809","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:13:30Z","timestamp":1773951210000},"page":"e80809-e80809","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning\u2013Based Risk Prediction for Coronary Heart Disease Complicated by Hyperhomocysteinemia: Retrospective Study"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0965-619X","authenticated-orcid":false,"given":"Ming-Yuan","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2013-4201","authenticated-orcid":false,"given":"Meng-Ke","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3917-6674","authenticated-orcid":false,"given":"Hai-long","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4583-8301","authenticated-orcid":false,"given":"Yi-zhuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1725-7825","authenticated-orcid":false,"given":"Hai-feng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-4041","authenticated-orcid":false,"given":"Xiao-hui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1055\/a-1952-1946","article-title":"Hyperhomocysteinemia in cardiovascular diseases: revisiting observational studies and clinical trials","volume":"123","author":"Gu\u00e9ant","journal-title":"Thromb Haemost"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.3389\/fnut.2019.00049","article-title":"Hyperhomocysteinemia as a risk factor and potential nutraceutical target for certain pathologies","volume":"6","author":"Tinelli","journal-title":"Front Nutr"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.biochi.2020.02.012","article-title":"Mechanisms of homocysteine-induced damage to the endothelial, medial and adventitial layers of the arterial wall","volume":"173","author":"Balint","journal-title":"Biochimie"},{"issue":"21","key":"R4","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20215293","article-title":"Immunobiology of atherosclerosis: a complex net of interactions","volume":"20","author":"Herrero-Fernandez","journal-title":"Int J Mol Sci"},{"issue":"11","key":"R5","doi-asserted-by":"publisher","DOI":"10.3390\/cells14110770","article-title":"Understanding atherosclerotic plaque cellular composition: recent advances driven by single cell omics","volume":"14","author":"Cetin","journal-title":"Cells"},{"issue":"5","key":"R6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0213653","article-title":"Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants","volume":"14","author":"Alaa","journal-title":"PLoS One"},{"issue":"11","key":"R7","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14111101","article-title":"Ultrasound carotid plaque score and severity of coronary artery disease assessed by computed tomography angiography in patients with arterial hypertension","volume":"14","author":"Wysocki","journal-title":"Diagnostics (Basel)"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rico.2025.100560","article-title":"Interactive cardiovascular disease prediction system using learning techniques: Insights from extensive experiments","volume":"19","author":"Pal","journal-title":"Results Control Optim"},{"issue":"6","key":"R9","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1007\/s11053-023-10249-6","article-title":"A novel scheme for mapping of MVT\u2011type Pb\u2013Zn prospectivity: LightGBM, a highly efficient gradient boosting decision tree machine learning algorithm","volume":"32","author":"Hajihosseinlou","journal-title":"Nat Resour Res"},{"issue":"11","key":"R10","doi-asserted-by":"publisher","first-page":"4654","DOI":"10.3390\/app14114654","article-title":"Why are explainable AI methods for prostate lesion detection rated poorly by radiologists?","volume":"14","author":"Gulum","journal-title":"Appl Sci (Basel)"},{"issue":"10","key":"R11","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000041766","article-title":"Big data analytics and machine learning in hematology: transformative insights, applications and challenges","volume":"104","author":"Obeagu","journal-title":"Medicine (Baltimore)"},{"issue":"4","key":"R12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174944","article-title":"Can machine-learning improve cardiovascular risk prediction using routine clinical data?","volume":"12","author":"Weng","journal-title":"PLoS One"},{"issue":"7","key":"R13","doi-asserted-by":"publisher","DOI":"10.1161\/HCQ.0000000000000141","article-title":"Correction to: 2025 AHA\/ACC clinical performance and quality measures for patients with chronic coronary disease: a report of the American College of Cardiology\/American Heart Association joint committee on performance measures","volume":"18","author":"Williams","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"38","key":"R14","doi-asserted-by":"publisher","first-page":"3720","DOI":"10.1093\/eurheartj\/ehad191","article-title":"2023 ESC Guidelines for the management of acute coronary syndromes","volume":"44","author":"Byrne","journal-title":"Eur Heart J"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1093\/eurjpc\/zwab154","article-title":"2021 ESC Guidelines on cardiovascular disease prevention in clinical practice","volume":"29","author":"Visseren","journal-title":"Eur J Prev Cardiol"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-016-0267-3","article-title":"No rationale for 1 variable per 10 events criterion for binary logistic regression analysis","volume":"16","author":"van Smeden","journal-title":"BMC Med Res Methodol"},{"issue":"8","key":"R17","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1177\/0962280218784726","article-title":"Sample size for binary logistic prediction models: beyond events per variable criteria","volume":"28","author":"van Smeden","journal-title":"Stat Methods Med Res"},{"issue":"7","key":"R18","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1002\/sim.7992","article-title":"Minimum sample size for developing a multivariable prediction model: part II\u2014binary and time-to-event outcomes","volume":"38","author":"Riley","journal-title":"Stat Med"},{"issue":"7","key":"R19","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1002\/sim.7993","article-title":"Minimum sample size for developing a multivariable prediction model: part I\u2014continuous outcomes","volume":"38","author":"Riley","journal-title":"Stat Med"},{"issue":"6","key":"R20","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: the Framingham Heart study","volume":"117","author":"D\u2019Agostino","journal-title":"Circulation"},{"issue":"25 Suppl 2","key":"R21","doi-asserted-by":"publisher","first-page":"S49","DOI":"10.1161\/01.cir.0000437741.48606.98","article-title":"2013 ACC\/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology\/American Heart Association Task Force on Practice Guidelines","volume":"129","author":"Goff","journal-title":"Circulation"},{"key":"R22","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.769857","article-title":"High perceived stress may shorten activated partial thromboplastin time and lead to worse clinical outcomes in patients with coronary heart disease","volume":"8","author":"Yin","journal-title":"Front Cardiovasc Med"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e80809","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:14:28Z","timestamp":1782843268000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e80809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,19]]},"references-count":22,"URL":"https:\/\/doi.org\/10.2196\/80809","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,19]]},"article-number":"v14i3e80809"}}