Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine

Journal article : Review

Background: Prediction of suboptimal growth allows early intervention that can improve outcomes for developing fetus’ as well as infants and children. We investigate the risk of bias in statistical or machine learning models to predict the height or weight of a fetus, infant or child under 20 years of age to inform the current standard of research and provide insight into why equations developed over 30 years ago are still recommended for use by national professional bodies. Methods: We systematically searched MEDLINE and EMBASE for peer reviewed original research studies published in 2022. We included studies if they developed or validated a multivariable model to predict height or weight of an individual using two or more variables, excluding studies assessing imaging or using genetics or metabolomics information. Risk of bias was assessed for all prediction models and analyses using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: Sixty-four studies were included, in which we assessed the development of 180 models and validation of 61 models. Sample size was only considered in 10% of developed models and 13% of validated models. Despite height and weight being continuous variables, 77% of models developed predicted a dichotomised outcome variable. Registration: The review was registered on PROSPERO (ID: CRD42023421146), the International prospective register of systematic reviews on 26/4/2023.

Authors: Lawrence, NR; Bacila, I; Tonge, J; Tucker, A; Dawson, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Diagnostic and Prognostic Research
• Volume: 9
• Issue: 1
• Article number: 32
• Publication date: 2025-12-15
• Acceptance date: 2025-12-01
• DOI: 10.1186/s41512-025-00215-6
• EISSN: 2397-7523
• ISSN: 2397-7523
• Language: English
• Keywords: Paediatrics; Height; Weight; Statistical modelling; Growth; Prediction modelling; Risk of bias; Obstetrics; Machine learning; Systematic review
• Subtype: Review