Development and external validation of an interpretable machine learning model for diagnosing coronary heart disease in patients with type 2 diabetes and MASLD.
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| Title: | Development and external validation of an interpretable machine learning model for diagnosing coronary heart disease in patients with type 2 diabetes and MASLD. |
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| Authors: | Deng C; Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China., Feng L; Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China., Li T; Department of Respiratory and Critical Care Medicine, Guangxi Hospital of the First Affiliated Hospital, Sun Yat-sen University, Nanning, Guangxi, China., Wei S; Clinical Research Center of Guangxi Academy of Medical Sciences, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China., Zhu H; Department of Clinical Laboratory, Nantong Sixth People's Hospital Affiliated to Shanghai University, Nantong, Jiangsu, China., Lu J; Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China. |
| Source: | Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2026 May 15; Vol. 17, pp. 1830594. Date of Electronic Publication: 2026 May 15 (Print Publication: 2026). |
| Publication Type: | Journal Article; Validation Study |
| Journal Info: | Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101555782 Publication Model: eCollection Cited Medium: Print ISSN: 1664-2392 (Print) Linking ISSN: 16642392 NLM ISO Abbreviation: Front Endocrinol (Lausanne) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1664-2392 |
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| DOI: | 10.3389/fendo.2026.1830594 |
