Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model-Reference-Cited by-同舟云学术

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model

Published:2023-11 Issue: Volume:179 Page:105228
ISSN:1386-5056
Container-title:International Journal of Medical Informatics
language:en
Short-container-title:International Journal of Medical Informatics

Author:

Belsti Yitayeh^ORCID,Moran Lisa,Du Lan^ORCID,Mousa Aya^ORCID,De Silva Kushan,Enticott Joanne^ORCID,Teede Helena

Funder

National Health and Medical Research Council

Publisher

Elsevier BV

Subject

Health Informatics

Reference53 articles.

1. Gestational diabetes mellitus (GDM) [internet]; 2019 [cited Nov 29 2022]. Available from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/diabetes/gestational-diabetes.

2. Organizing Committee. Summary and recommendations of the fourth international workshop-conference on gestational diabetes mellitus;Metzger;Diabetes Care,1998

3. IDF diabetes Atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by international association of diabetes in pregnancy study Group’s criteria;Wang;Diabetes Res. Clin. Pract.,2022

4. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040;Ogurtsova;Diabetes Res. Clin. Pract.,2017

5. IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045;Cho;Diabetes Res. Clin. Pract.,2018

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning;Resuscitation;2024-09

2. Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters;Franklin Open;2024-09

3. Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration;Applied Sciences;2024-08-23

4. The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation;Reproductive Biology and Endocrinology;2024-07-10

5. Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study;Annals of Operations Research;2024-07-08