Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts
This study developed the first set of ML-based risk prediction models to identify infants' risk of experiencing RWG by the age of 1 year with acceptable accuracy. The models could be feasibly integrated into routine child growth monitoring and may facilitate population-wide early obesity risk assessment in primary health care.
Authors
- Kylie D. Hesketh (ORCID: https://orcid.org/0000-0002-2702-7110)
- Rebecca Byrne (ORCID: https://orcid.org/0000-0002-0096-3320)
- Jodie M Dodd (ORCID: https://orcid.org/0000-0002-6363-4874)
- Peter Vuillermin (ORCID: https://orcid.org/0000-0002-6580-0346)
- Rachel Laws (ORCID: https://orcid.org/0000-0003-4328-1116)
- Louise Alison Baur (ORCID: https://orcid.org/0000-0002-4521-9482)
- Li Ming Wen (ORCID: https://orcid.org/0000-0003-1381-4022)
- Rachael Waring Taylor (ORCID: https://orcid.org/0000-0001-9516-2253)
- Miaobing Zheng (ORCID: https://orcid.org/0000-0002-4151-3502)
- Anne‐Louise Ponsonby (ORCID: https://orcid.org/0000-0002-6581-3657)
- Yuxin Zhang (ORCID: https://orcid.org/0000-0002-7636-4084)
Institutions
- The University of Sydney (AU)
- Barwon Health (AU)
- Royal Children's Hospital (AU)
- Deakin University (AU)
- Queensland University of Technology (AU)
- UNSW Sydney (AU)
- The University of Adelaide (AU)
- University of Otago (NZ)
Publication Details
- Journal
- JMIR Public Health and Surveillance
- Published
- 2025-06-18
- DOI
- https://doi.org/10.2196/69220
- Citations
- 2
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 4.11