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

Formative Evaluation of Digital Health Interventions (2501) Artificial Intelligence (1432) Machine Learning (1506) Clinical Informatics (1050)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53654, first published .
Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches

Authors of this article:

Liang Zhang1 Author Orcid Image ;   Yueqing Huang1 Author Orcid Image ;   Min Huang1 Author Orcid Image ;   Chun-Hua Zhao1 Author Orcid Image ;   Yan-Jun Zhang2 Author Orcid Image ;   Yi Wang3 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. 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