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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.
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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  Data: Machine learning-based model for predicting metabolic dysfunction-associated steatotic liver disease using non-invasive parameters in young adults.
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  Data: <searchLink fieldCode="AU" term="%22Song+K%22">Song K</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kwon+YJ%22">Kwon YJ</searchLink>; Department of Family Medicine, Yonsei University College of Medicine, Yongin Severance Hospital, Yongin-si, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+E%22">Lee E</searchLink>; Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Youn+YH%22">Youn YH</searchLink>; Department of Healthcare Research Team, Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Baik+SJ%22">Baik SJ</searchLink>; Department of Healthcare Research Team, Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+HS%22">Lee HS</searchLink>; Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Chae+HW%22">Chae HW</searchLink>; Department of Pediatrics, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101555782%22">Frontiers in endocrinology</searchLink> [Front Endocrinol (Lausanne)] 2025 Dec 16; Vol. 16, pp. 1701729. <i>Date of Electronic Publication: </i>2025 Dec 16 (<i>Print Publication: </i>2025).
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        Value: 10.3389/fendo.2025.1701729
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      – TitleFull: Machine learning-based model for predicting metabolic dysfunction-associated steatotic liver disease using non-invasive parameters in young adults.
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              Text: 2025 Dec 16
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