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

Theme Issue: Reproductive Informatics (58) Clinical Information and Decision Making (1584) Artificial Intelligence (1641) Decision Support for Health Professionals (1310) Clinical Informatics (1121) Machine Learning (1662)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44081, first published .
Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies

Authors of this article:

Yang Ren1 Author Orcid Image ;   Dezhi Wu2 Author Orcid Image ;   Yan Tong1 Author Orcid Image ;   Ana López-DeFede3 Author Orcid Image ;   Sarah Gareau3 Author Orcid Image
Article Authors Cited by (17) Tweetations (2) Metrics

Journals

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  9. Mahant M, Pellakuri V. Data-driven insights using decision trees and K-nearest neighbors (KNN) for improving child and women's health. Multimedia Tools and Applications 2024 View
  10. Hu Z, Li X, Yuan Y, Xu Q, Zhang W, Lei H. Development and validation of machine learning models for predicting venous thromboembolism in colorectal cancer patients: A cohort study in China. International Journal of Medical Informatics 2025;195:105770 View
  11. Nawaz A, Ahmad A, Khan S, Masud M, Ghenimi N, Ahmed L, Aung Z. An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection. PLOS ONE 2025;20(1):e0317843 View
  12. Brown C, Thomsen M, Amick B, Tilford J, Bryant-Moore K, Gomez-Acevedo H. Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity. Journal of Racial and Ethnic Health Disparities 2025 View
  13. Gao J, Jie X, Yao Y, Xue J, Chen L, Chen R, Chen J, Cheng W. Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model. JMIR Pediatrics and Parenting 2025;8:e59377 View
  14. Cao J, Wang J, Ma Z, Ding K, Yuan L, Wu G, Huang J. Detection of cervical cancer with imbalanced class distribution based on Raman spectroscopy and novel resampling techniques. Measurement 2025;251:117311 View
  15. Victor A, Almeida F, Xavier S, Rondó P. Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study. BMC Pregnancy and Childbirth 2025;25(1) View