Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators
Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies.
Authors
- Vida Pahlevani
- Mehdi Azizmohammad Looha (ORCID: https://orcid.org/0000-0002-0700-1431)
- Farid Zayeri (ORCID: https://orcid.org/0000-0002-7791-8122)
- Reza Homayounfar (ORCID: https://orcid.org/0000-0001-5398-9519)
- Mojtaba Farjam (ORCID: https://orcid.org/0000-0003-4826-2846)
- Morteza Mohammadzadeh (ORCID: https://orcid.org/0000-0002-2258-3388)
- Fatemeh Masaebi (ORCID: https://orcid.org/0000-0002-8439-5024)
Institutions
- Tarbiat Modares University (IR)
- Iran University of Medical Sciences (IR)
- National Nutrition and Food Technology Research Institute (IR)
- Fasa University of Medical Sciences (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- Archives of Iranian Medicine
- Published
- 2024-10-01
- DOI
- https://doi.org/10.34172/aim.31269
- Citations
- 4
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.55