Explainable Artificial Intelligence Modeling of Social Participation in Children with Intellectual Disabilities: A SHAP-Based CatBoost Analysis of Adaptive Behavior, Communication Competence, and Parental Involvement
Keywords:
Social Participation, Intellectual Disabilities, Adaptive Behavior, Communication Competence, Parental InvolvementAbstract
The present study aimed to develop and interpret an explainable artificial intelligence model of social participation in children with intellectual disabilities by examining the predictive contributions of adaptive behavior, communication competence, and parental involvement using a SHAP-based CatBoost algorithm. This cross-sectional predictive modeling study was conducted among 412 children with mild to moderate intellectual disabilities aged 8–16 years who were recruited from special education schools, rehabilitation centers, and inclusive educational settings across Hungary. Social participation was assessed using the Participation and Environment Measure for Children and Youth (PEM-CY), while adaptive behavior, communication competence, and parental involvement were measured using standardized psychometric instruments. Following data preprocessing and screening procedures, a CatBoost machine learning model was developed to predict social participation outcomes. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To enhance interpretability, SHapley Additive exPlanations (SHAP) analyses were conducted to determine the relative importance and direction of predictor contributions. Pearson correlation analyses demonstrated significant positive associations between social participation and adaptive behavior (r = .781, p < .001), communication competence (r = .724, p < .001), and parental involvement (r = .647, p < .001). The CatBoost model exhibited excellent predictive performance, explaining 89.2% of the variance in social participation within the test dataset (R² = .892), with RMSE = 4.97, MAE = 3.89, and MAPE = 5.84%. SHAP analyses identified adaptive behavior as the strongest predictor of social participation (42.7% relative importance), followed by communication competence (32.8%) and parental involvement (19.7%). Together, these three variables accounted for more than 95% of the model’s total explanatory influence. Furthermore, SHAP dependence analyses revealed nonlinear interactions, indicating that the positive effects of adaptive behavior became increasingly pronounced at higher levels of communication competence and parental involvement. The findings demonstrate that social participation among children with intellectual disabilities is primarily influenced by adaptive behavior, communication competence, and parental involvement. The SHAP-based CatBoost model provided highly accurate and interpretable predictions, highlighting the value of explainable artificial intelligence for understanding participation outcomes in developmental disability research. These results support the development of family-centered and skills-focused interventions aimed at enhancing social inclusion and community participation among children with intellectual disabilities.
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