A prediction model for childhood obesity risk using the machine learning method: a panel study on Korean children

Heemoon Lim, Hyejung Lee, Joungyoun Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Article number10122
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023

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