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

被引:3
作者
Choi, Hangnyoung [1 ,2 ]
Kim, Jae Han [3 ]
Kim, Hwiyoung [4 ,5 ]
Cheon, Keun-Ah [1 ,2 ]
机构
[1] Yonsei Univ, Severance Hosp, Dept Child & Adolescent Psychiat, Coll Med, Seoul, South Korea
[2] Yonsei Univ, Yonsei Univ Hlth Syst, Inst Behav Sci Med, Coll Med, Seoul, South Korea
[3] Yonsei Univ, Yonsei Univ Hlth Syst, Severance Hosp, Coll Med, Seoul, South Korea
[4] Yonsei Univ, Coll Med, Dept Radiol, Ctr Clin Imaging Data Sci, Seoul, South Korea
[5] Yonsei Univ, Dept Biomed Syst Informat, Coll Med, Seoul, South Korea
关键词
autism spectrum disorder; parenting stress; artificial intelligence; machine learning; predictor; DEPRESSION; MMPI-2;
D O I
10.3389/fnins.2023.1229155
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Introduction: Previous 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. Methods: Study 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. Results: A 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. Conclusion: By 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.
引用
收藏
页数:13
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