{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:48:33Z","timestamp":1774424913937,"version":"3.50.1"},"reference-count":43,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Obesity"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Children with overweight (body mass index [BMI] between the 85th and 95th percentiles) represent a critical target for obesity prevention, with obesity progression rates 10\u2010 to 20\u2010fold higher than normal\u2010weight peers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study developed machine learning models to predict obesity in this high\u2010risk group using anthropometric and bioelectrical impedance analysis parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analysed longitudinal data from 2801 overweight Korean children aged 7\u20139\u2009years. XGBoost models integrated anthropometric measurements, bioelectrical impedance\u2010derived body composition, standardised deviation scores and growth velocity parameters to predict obesity (BMI \u2265\u200995th percentile). Sex\u2010stratified models were evaluated using area under the receiver operating characteristic curve (AUROC) with bootstrap validation. Shapley Additive exPlanations (SHAP) identified key predictive features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Obesity developed in 32.3% of males and 25.4% of females during follow\u2010up. Models achieved AUROC scores of 0.671 (95% CI: 0.619\u20130.721) for males and 0.652 (95% CI: 0.589\u20130.700) for females. Key predictors included standardised weight and adiposity measures, height\u2010adjusted skeletal muscle mass and growth velocity parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning models demonstrated effective predictive performance for obesity in children with overweight. Incorporating standardised adiposity measures and growth velocity beyond static anthropometric data provides a robust framework for risk stratification in this high\u2010risk population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijpo.70105","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T01:44:02Z","timestamp":1773884642000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Prediction of Obesity Development in Children With Overweight Using Longitudinal Body Composition Data"],"prefix":"10.1111","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3031-4011","authenticated-orcid":false,"given":"Dohyun","family":"Chun","sequence":"first","affiliation":[{"name":"College of Business Administration, Kangwon National University  Chuncheon Gangwon\u2010do Korea"},{"name":"Research Team, GP Co. Ltd.  Gwangmyeong\u2010si Gyeonggi\u2010do Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young\u2010Jun","family":"Rhie","sequence":"additional","affiliation":[{"name":"Department of Pediatrics Korea University College of Medicine  Seoul Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Sawyer","sequence":"additional","affiliation":[{"name":"Department of Biological and Biomedical Sciences Bryant University  Smithfield Rhode Island USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongho","family":"Kang","sequence":"additional","affiliation":[{"name":"Research Team, GP Co. Ltd.  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Gwangmyeong\u2010si Gyeonggi\u2010do Korea"},{"name":"College of Humanities &amp; Social Sciences Convergence, Yonsei University  Wonju Gangwon\u2010do Korea"},{"name":"Yonsei Institute of AI Data Convergence Science, Yonsei University  Wonju Gangwon\u2010do Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"World Health Organization \u201cObesity and Overweight \u201d2025accessed August 28 2025 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