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

Public Health Informatics (147) Clinical Information and Decision Making (2079) Cardiovascular Disease Prevention (216) Artificial Intelligence (2365) Machine Learning (2241) Computer-Aided Diagnosis (280) Decision Support for Health Professionals (1644)

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 (12) Tweetations (2) Metrics

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