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

Machine Learning (2173) Artificial Intelligence in Diabetes Care and Prevention (58) Diabetes Prevention (124) Decision Support for Health Professionals (1594)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72238, first published .
Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

Authors of this article:

Ting Peng1 Author Orcid Image ;   Rujia Miao1 Author Orcid Image ;   Hao Xiong2 Author Orcid Image ;   Yanhui Lin1 Author Orcid Image ;   Duzhen Fan2 Author Orcid Image ;   Jiayi Ren2 Author Orcid Image ;   Jiangang Wang1 Author Orcid Image ;   Yuan Li2 Author Orcid Image ;   Jianwen Chen2 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Nassar M. Privacy-Preserving, Multi-Objective Whale-Bat Optimized XGBoost for Early Diagnosis of Insulin Resistance in Non-Diabetic Women: Development and Validation Using NHANES 2007–2020. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 2025 View
  2. Duan Y, Lei C, Xu S, Wang J, Li J, Fu Y, Xi Y, An J, Chen R. Predicting perceived likelihood of future suicide attempts in youth with non-suicidal self-injury: A machine learning approach using LightGBM and SHAP. Journal of Affective Disorders 2026;398:120966 View
  3. Yong W, Peng D, Ye K, Gao J, Cao R. Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease. Frontiers in Endocrinology 2026;17 View