Machine learning-based model for predicting metabolic dysfunction-associated steatotic liver disease using non-invasive parameters in young adults.
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| Title: | Machine learning-based model for predicting metabolic dysfunction-associated steatotic liver disease using non-invasive parameters in young adults. |
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| Authors: | Song K; Department of Pediatrics, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea., Kwon YJ; Department of Family Medicine, Yonsei University College of Medicine, Yongin Severance Hospital, Yongin-si, Republic of Korea., Lee E; Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea., Youn YH; Department of Healthcare Research Team, Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea., Baik SJ; Department of Healthcare Research Team, Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea., Lee HS; Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea., Chae HW; Department of Pediatrics, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea. |
| Source: | Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2025 Dec 16; Vol. 16, pp. 1701729. Date of Electronic Publication: 2025 Dec 16 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| 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.2025.1701729 |
