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

Public Health Informatics (135) Clinical Information and Decision Making (1915) Cardiovascular Disease Prevention (206) Artificial Intelligence (2129) Machine Learning (2076) Computer-Aided Diagnosis (251) Decision Support for Health Professionals (1539)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47095, first published .
Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study

Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study

Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study

Authors of this article:

Yuhan Deng1, 2 Author Orcid Image ;   Yuan Ma3 Author Orcid Image ;   Jingzhu Fu4, 5, 6 Author Orcid Image ;   Xiaona Wang7 Author Orcid Image ;   Canqing Yu4, 5, 6, 8 Author Orcid Image ;   Jun Lv4, 5, 6, 8 Author Orcid Image ;   Sailimai Man2, 4, 5, 6 Author Orcid Image ;   Bo Wang2, 5, 8 Author Orcid Image ;   Liming Li4, 5, 6, 8 Author Orcid Image
Article Authors Cited by (7) Tweetations (2) Metrics

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  3. Zhang Y, Xue H, Xia H, Jiang X. Prediction models for cognitive frailty in community-dwelling older adults: A scoping review. Geriatric Nursing 2024;60:448 View
  4. Deng L, Wang S, Wan D, Zhang Q, Shen W, Liu X, Zhang Y. Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression. International Journal of General Medicine 2025;Volume 18:509 View
  5. Wang F, Mao Y, Sun J, Yang J, Xiao L, Huang Q, Wei C, Gou Z, Zhang K. Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes. Scientific Reports 2025;15(1) View
  6. Singh S, Kaur P, Kaur I, Singh G, Kaur S, Kaur P. A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health. Scientific Reports 2025;15(1) View
  7. Zhai S, Wang X, Zhang H, Zuo Y. Predicting carotid plaques in metabolic dysfunction-associated steatotic liver disease using machine learning and SHAP interpretation. Scientific Reports 2025;15(1) View