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

Theme Issue 2022: Reproductive Informatics (52) Clinical Information and Decision Making (1407) Artificial Intelligence (1371) Decision Support for Health Professionals (1185) Clinical Informatics (1022) Machine Learning (1465)

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 (10) Tweetations (2) Metrics

Journals

  1. Ranjbar A, Montazeri F, Farashah M, Mehrnoush V, Darsareh F, Roozbeh N. Machine learning-based approach for predicting low birth weight. BMC Pregnancy and Childbirth 2023;23(1) View
  2. Lee H, Cho J, Park J, Lee H, Fond G, Boyer L, Kim H, Park S, Cho W, Lee H, Lee J, Yon D. Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts. Journal of Medical Internet Research 2024;26:e51473 View
  3. Alabbad D, Ajibi S, Alotaibi R, Alsqer N, Alqahtani R, Felemban N, Rahman A, Aljameel S, Ahmed M, Youldash M. Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach. Machine Learning and Knowledge Extraction 2024;6(2):770 View
  4. Zhang Y, Xu J, Zhang C, Zhang X, Yuan X, Ni W, Zhang H, Zheng Y, Zhao Z. Community screening for dementia among older adults in China: a machine learning-based strategy. BMC Public Health 2024;24(1) View
  5. Xu L, Li C, Zhang J, Guan C, Zhao L, Shen X, Zhang N, Li T, Yang C, Zhou B, Bu Q, Xu Y. Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence. European Journal of Medical Research 2024;29(1) View
  6. Wang X, Xu L, Guan C, Xu D, Che L, Wang Y, Man X, Li C, Xu Y. Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients. Frontiers in Medicine 2024;11 View
  7. Zhu J, Pu S, He J, Su D, Cai W, Xu X, Liu H. Processing imbalanced medical data at the data level with assisted-reproduction data as an example. BioData Mining 2024;17(1) View
  8. Xu L, Li C, Gao S, Zhao L, Guan C, Shen X, Zhu Z, Guo C, Zhang L, Yang C, Bu Q, Zhou B, Xu Y. Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study. JMIR Medical Informatics 2024;12:e52837 View
  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