Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models

Abstract

IntroductionPrevious studies have investigated predictive factors for parenting stress in caregivers of autism spectrum disorder (ASD) patients using traditional statistical approaches, but their study settings and results were inconsistent. Herein, this study aimed to identify major predictors for parenting stress in this population by developing explainable machine learning models.MethodsStudy participants were collected from the Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, the Republic of Korea between March 2016 and October 2020. A total of 36 model features were used, which include subscales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) for caregivers’ psychopathology, Social Responsiveness Scale-2 for core symptoms, and Child Behavior Checklist (CBCL) for behavioral problems. Machine learning classifiers [eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression, and support vector machine (SVM) classifier] were generated to predict severe total parenting stress and its subscales (parental distress, parent-child dysfunctional interaction, and difficult child). Model performance was assessed by area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. We utilized the SHapley Additive exPlanations tree explainer to investigate major predictors.ResultsA total of 496 participants were included [mean age of ASD patients 6.39 (SD 2.24); 413 men (83.3%)]. The best-performing models achieved an AUC of 0.831 (RF model; 95% CI 0.740–0.910) for parental distress, 0.814 (SVM model; 95% CI 0.720–0.896) for parent-child dysfunctional interaction, 0.813 (RF model; 95% CI 0.724–0.891) for difficult child, and 0.862 (RF model; 95% CI 0.783–0.930) for total parenting stress on the test set. For the total parenting stress, ASD patients’ aggressive behavior and anxious/depressed, and caregivers’ depression, social introversion, and psychasthenia were the top 5 leading predictors.ConclusionBy using explainable machine learning models (XGBoost and RF), we investigated major predictors for each subscale of the parenting stress index in caregivers of ASD patients. Identified predictors for parenting stress in this population might help alert clinicians whether a caregiver is at a high risk of experiencing severe parenting stress and if so, providing timely interventions, which could eventually improve the treatment outcome for ASD patients.

Correlations of Physical and Psychological Health in Parents of Individuals With Autism Spectrum Disorder

“…9 Furthermore, parental depression, social introversion, anxiety, and child behavior problems have been identified as major predictors of parenting stress in a recent study using machine learning for analysis. 11 In a recent study using latent profile analysis, increased parental depression was predicted by higher levels of perceived daily stressors, especially for children with the most severe challenging behaviors. 12 Along with stress and depression, higher parent anxiety is associated with more challenging behaviors in individuals with ASD.…”