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

Machine Learning (3028) Clinical Information and Decision Making (3512) Decision Support for Health Professionals (2107) Gestational Diabetes (61) Pregnancy Information, Education and Lifestyle Interventions (468)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71539, first published .
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Interpretable Machine Learning for Predicting Adverse Pregnancy Outcomes in Gestational Diabetes: Retrospective Cohort Study

Interpretable Machine Learning for Predicting Adverse Pregnancy Outcomes in Gestational Diabetes: Retrospective Cohort Study

Authors of this article:

Jiaxi Li1 Author Orcid Image ;   Xiali Liu2 Author Orcid Image ;   Shenyang He1 Author Orcid Image ;   Yan Ren3 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. Makhachev D, Musapirov I, Magomedova M, Abakarov M, Ametov D. Artificial intelligence in diabetology: systematizing clinical evidence and prospects for implementation. International journal of Innovative Medicine 2025;(2):4 View
  2. Wu Y. Machine Learning‐Based Risk Prediction Models for Pregnancy‐Related Syndromes. Birth Defects Research 2026;118(3) View
  3. Powar O, Kadiya A, Pawar P, Kanchan M. Improved classification of term and preterm births from electrohysterogram signals using machine learning with model error features. Discover Applied Sciences 2026;8(5) View

Conference Proceedings

  1. Begum S, P.Jahnavi , B.Keerthika , Rajesh Y, Kumar N. 2026 8th International Conference on Inventive Material Science and Applications (ICIMA). IntelliFetus: A Machine Learning Framework for Automated Fetal Health Classification View