Development of model to predict problem behavior in 11-year-old children based on home environment and demographic factors

Objectives The purpose of this study is to evaluate the performance of a prediction model to determine whether the home environment and demographic factors of 10-year-old children predict problem behaviors one year later and to identify important predictive factors. Methods The research data used the 11th (data collection in 2018) and 12th (data collection in 2019) data of the Korean Children's Panel. The input variables are demographic variables, parenting behavior and mental health-related variables reported by parents, and family relationship variables reported by children in the 11th data of 1,177 10-year-old children (574 girls and 603 boys). The output variable is whether the children at the age of 11 in the 12th data have clinical-level internalizing and externalizing behavior problems (T≥64). The XGBoost algorithm was used to develop the prediction model. Results The analysis results are as follows. First, the prediction performance of the XGBoost models predicting clinical-level problem behaviors was very good, with AUC = 1.00 for both internalizing and externalizing behavior problems. Second, the key predictor of clinical-level problem behaviors was the mother’s perceived marital conflict for internalizing behavior problems, and the mother’s permissive parenting style for externalizing behavior problems. Conclusions The problem behavior prediction model for school-age children, developed using machine learning and data from the Korean Children's Panel, is highly accurate in predicting whether problem behavior will reach clinical levels a year in advance. This highlights the importance of supporting marital relationships and positive parenting practices to prevent problematic behaviors in children.