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

Formative Evaluation of Digital Health Interventions (2991) Artificial Intelligence (1922) Machine Learning (1889) Clinical Informatics (1220)

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 (2) 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

Conference Proceedings

  1. Maurya S, Khatoon A, Hassan A, Badhan A, Rakhra M, Sarkar T. 2025 International Conference on Networks and Cryptology (NETCRYPT). Advanced Diagnostic Framework for Liver Disease Identification Utilizing Integrated Ensemble Machine Learning Models View