ArticleAmerican journal of preventive medicine2021
Use of Machine Learning to Determine the Information Value of a BMI Screening Program.
Article in American journal of preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.Frontiers in nutrition · 2026Article
- A lifestyle-based prediction model for obesity in Chinese adolescent students.Frontiers in sports and active living · 2025Article
- Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach.Frontiers in big data · 2024Article
- Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2023Article
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
introductionChildhood obesity continues to be a significant public health issue in the U.S. and is associated with short- and long-term adverse health outcomes. A number of states have implemented school-based BMI screening programs. However, these programs have been criticized for not being effective in improving students' BMI or reducing childhood obesity. One potential benefit, however, of screening programs is the identification of younger children at risk of obesity as they age.
methodsThis study used a unique panel data set from the BMI screening program for public school children in the state of Arkansas collected from 2003 to 2004 through the 2018-2019 academic years and analyzed in 2020. Machine learning algorithms were applied to understand the informational value of BMI screening. Specifically, this study evaluated the importance of BMI information during kindergarten to the accurate prediction of childhood obesity by the 4th grade.
resultsKindergarten BMI z-score is the most important predictor of obesity by the 4th grade and is much more important to prediction than sociodemographic and socioeconomic variables that would otherwise be available to policymakers in the absence of the screening program. Including the kindergarten BMI z-score of students in the model meaningfully increases the accuracy of the prediction.
conclusionsData from the Arkansas BMI screening program greatly improve the ability to identify children at greatest risk of future obesity to the extent that better prediction can be translated into more effective policy and better health outcomes. This is a heretofore unexamined benefit of school-based BMI screening.
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Registered trials
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