{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T05:38:30Z","timestamp":1792647510514,"version":"4.5.0"},"reference-count":50,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"20","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,10,20]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Coronary heart disease (CHD) with concomitant tumors is increasingly prevalent and is associated with a higher mortality. We aimed to develop 2 predictive models to enable early identification of tumor risk in patients with CHD and mortality risk in those with CHD and cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">We analyzed 134\u2009141 patients with CHD treated at Tongji Hospital (Huazhong University of Science and Technology, Wuhan, Hubei, China) during 2001 to 2023. Data collected from the First Hospital of Peking University (Beijing, China) were used for external validation. We developed and evaluated 6 machine learning models (extreme gradient boosting, light gradient boosting machine, random forest, neural network, decision tree, and logistic regression). Model explainability was improved using Shapley additive explanation, and an online prediction platform was subsequently implemented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">For the model of predicting tumor risk in patients with CHD, extreme gradient boosting showed the best performance in the test set (area under the receiver operating characteristic curve =0.935), achieving the lowest Brier score (0.080). Its sensitivity, accuracy, and F1 score were 0.740, 0.882, and 0.751, respectively. Meanwhile, light gradient boosting machine performed well for in\u2010hospital mortality prediction in the test set (area under the receiver operating characteristic curve =0.965), achieving the lowest Brier score (0.019), and the highest sensitivity (0.677), and F1 score (0.599). The 2 models also showed good performance in the external validation, suggesting a degree of generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">We constructed 2 predictive models to assess the risk of tumor and cancer\u2010associated mortality in patients with CHD. The web platform assists clinicians in decision\u2010making and patient management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.126.051025","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:58:42Z","timestamp":1791633522000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010Learning Prediction of Tumor Comorbidity and Cancer\u2010Associated Mortality in Patients With Coronary Heart Disease: A 2\u2010Stage Modeling Study"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3247-2995","authenticated-orcid":false,"given":"Wei","family":"Zheng","sequence":"first","affiliation":[{"name":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","place":["Wuhan, People\u2019s Republic of 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