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

Public Health Informatics (79) Clinical Information and Decision Making (1328) Cardiovascular Disease Prevention (114) Artificial Intelligence (1198) Machine Learning (1385) Computer-Aided Diagnosis (97) Decision Support for Health Professionals (1137)

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 (2) Tweetations (2) Metrics

Journals

  1. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
  2. Halushko O, Hurtovyi Y. Development and validation of a mathematical model for predicting the development of gastro-oesophageal reflux disease based on oesophagogastroduodenoscopy. Bulletin Of Medical And Biological Research 2024;6(1):15 View