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

Yang Ren   1 , MSc ;   Dezhi Wu   2 , PhD ;   Yan Tong   1 , PhD ;   Ana López-DeFede   3 , PhD ;   Sarah Gareau   3 , DrPH

1 Department of Computer Science, University of South Carolina, Columbia, SC, United States

2 Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States

3 The Institute of Families in Society, University of South Carolina, Columbia, SC, United States

Corresponding Author:

  • Dezhi Wu, PhD
  • Department of Integrated Information Technology
  • University of South Carolina
  • 550 Assembly Street
  • Columbia, SC, 29298
  • United States
  • Phone: 1 8033774691
  • Fax: 1 8037779564
  • Email: dezhiwu@cec.sc.edu