A LightGBM Analysis of Emotional Well-Being in Children with Autism Spectrum Disorder: Predictive Contributions of Sensory Sensitivity, Parent–Child Attachment, and Social Competence - Ozoliņa, Elīna; Dlamini, Nomvula; Atieno, Mercy (Psychological Research in Individuals with Exceptional Needs, 2026)
The present study aimed to investigate the predictive contributions of sensory sensitivity, parent–child attachment, and social competence to emotional well-being among children with Autism Spectrum Disorder (ASD) using a Light Gradient Boosting Machine (LightGBM) model and explainable artificial intelligence techniques. This cross-sectional predictive study was conducted among 428 children diagnosed with Autism Spectrum Disorder in South Africa. Emotional well-being served as the target variable, while sensory sensitivity, parent–child attachment, and social competence were entered as predictor variables. Data were collected using standardized caregiver-report instruments with established psychometric properties. After preliminary data screening and preprocessing, the dataset was randomly divided into training and testing subsets using an 80:20 ratio. A LightGBM algorithm was implemented to develop the predictive model, and hyperparameter optimization was performed through five-fold cross-validation. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). Feature importance analysis and Shapley Additive Explanations (SHAP) were employed to determine the relative contribution and direction of influence of each predictor variable. The LightGBM model demonstrated strong predictive performance, explaining 82.1% of the variance in emotional well-being within the testing dataset (R² = 0.821). Correlation analyses revealed significant positive associations between emotional well-being and both parent–child attachment (r = 0.68, p < .01) and social competence (r = 0.74, p < .01), whereas sensory sensitivity was significantly negatively associated with emotional well-being (r = −0.61, p < .01). Feature importance analysis indicated that social competence was the most influential predictor (39.8%), followed by parent–child attachment (33.3%) and sensory sensitivity (26.9%). SHAP analyses confirmed these findings, demonstrating that higher levels of social competence and stronger parent–child attachment increased predicted emotional well-being, whereas elevated sensory sensitivity reduced emotional well-being predictions. The findings indicate that emotional well-being among children with ASD is strongly shaped by social, relational, and sensory factors. Social competence emerged as the most influential predictor, highlighting the importance of adaptive social functioning for positive emotional outcomes. Secure parent–child attachment also contributed substantially to emotional well-being, while sensory sensitivity functioned as a significant risk factor. The high predictive accuracy of the LightGBM model demonstrates the value of machine learning approaches for identifying key determinants of emotional well-being and supports the development of targeted interventions focusing on social skills enhancement, family relationships, and sensory regulation to improve psychological outcomes among children with ASD.