ArticleScientific reports2023
Predicting body mass index in early childhood using data from the first 1000 days.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- PODiaCarD: a prototype of a digital twin platform for the management of pediatric obesity and related cardiometabolic complications.European journal of pediatrics · 2026Article
- Development and interpretation of a machine learning model for predicting body mass index in Chinese adolescents: a prospective cohort study.Frontiers in public health · 2025Article
- Obesity Prevention in Early Life (OPEL) study: linking longitudinal data to capture obesity risk in the first 1000 days.BMJ nutrition, prevention & health · 2024Article
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3 authors.
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Abstract
Few existing efforts to predict childhood obesity have included risk factors across the prenatal and early infancy periods, despite evidence that the first 1000 days is critical for obesity prevention. In this study, we employed machine learning techniques to understand the influence of factors in the first 1000 days on body mass index (BMI) values during childhood. We used LASSO regression to identify 13 features in addition to historical weight, height, and BMI that were relevant to childhood obesity. We then developed prediction models based on support vector regression with fivefold cross validation, estimating BMI for three time periods: 30-36 (N = 4204), 36-42 (N = 4130), and 42-48 (N = 2880) months. Our models were developed using 80% of the patients from each period. When tested on the remaining 20% of the patients, the models predicted children's BMI with high accuracy (mean average error [standard deviation] = 0.96[0.02] at 30-36 months, 0.98 [0.03] at 36-42 months, and 1.00 [0.02] at 42-48 months) and can be used to support clinical and public health efforts focused on obesity prevention in early life.
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