ArticleScientific reports2023
A prediction model for childhood obesity risk using the machine learning method: a panel study on Korean children.
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 9 papers.
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9 citing papers in PubMed.
- Routine Life-Course Health Records in Infancy Predict Being Overweight in Childhood and Adolescence: The TMM BirThree Cohort Study.Children (Basel, Switzerland) · 2026Article
- Development and external validation of an interpretable machine learning-based model for obesity risk prediction in 2-18-year-old children and adolescents in Beijing and Tangshan.Journal of global health · 2026Article
- Study of the Genetic Basis of Childhood and Adolescent Obesity with Stress Through the Analysis of Multidimensional Data with Machine Learning and Artificial Intelligence Tools.Advances in experimental medicine and biology · 2026Article
- 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
- National data meets AI: Machine learning for predicting overweight/obesity among ever-married Bangladeshi women.PloS one · 2026Article
- Development of a Deep Learning Model for Predicting Obesity Using Health Behavior Data of Elementary School Students.Iranian journal of public health · 2025Article
- Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis.Scientific reports · 2025Article
- A lifestyle-based prediction model for obesity in Chinese adolescent students.Frontiers in sports and active living · 2025Article
- Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997-2023).Immunity, inflammation and disease · 2024Article
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Abstract
Young children are increasingly exposed to an obesogenic environment through increased intake of processed food and decreased physical activity. Mothers' perceptions of obesity and parenting styles influence children's abilities to maintain a healthy weight. This study developed a prediction model for childhood obesity in 10-year-olds, and identify relevant risk factors using a machine learning method. Data on 1185 children and their mothers were obtained from the Korean National Panel Study. A prediction model for obesity was developed based on ten factors related to children (gender, eating habits, activity, and previous body mass index) and their mothers (education level, self-esteem, and body mass index). These factors were selected based on the least absolute shrinkage and selection operator. The prediction model was validated with an Area Under the Receiver Operator Characteristic Curve of 0.82 and an accuracy of 76%. Other than body mass index for both children and mothers, significant risk factors for childhood obesity were less physical activity among children and higher self-esteem among mothers. This study adds new evidence demonstrating that maternal self-esteem is related to children's body mass index. Future studies are needed to develop effective strategies for screening young children at risk for obesity, along with their mothers.
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