This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1407) Artificial Intelligence (1371) Machine Learning (1465) New Methods (944) Longitudinal and Cohort Studies in Public Health (309) Prevention and Health Promotion (986)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46891, first published .
Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Authors of this article:

Guoqing Huang1, 2 Author Orcid Image ;   Qiankai Jin1, 2 Author Orcid Image ;   Yushan Mao1 Author Orcid Image
Article Authors Cited by (4) Tweetations (6) Metrics

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  2. Lv J, Zhang Y, Li X, Guo H, Yang C. The burden of non-alcoholic fatty liver disease among working-age people in the Western Pacific Region, 1990–2019: an age–period–cohort analysis of the Global Burden of Disease study. BMC Public Health 2024;24(1) View
  3. Yang B, Lu H, Ran Y. Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation. Frontiers in Endocrinology 2024;15 View
  4. Liu L, Liang L, Luo Y, Han J, Lu D, Cai R, Sethi G, Mai S. Unveiling the Power of Gut Microbiome in Predicting Neoadjuvant Immunochemotherapy Responses in Esophageal Squamous Cell Carcinoma. Research 2024;7 View