Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models
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...
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| Format: | Article |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1229155/full |
| _version_ | 1797733152430489600 |
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| author | Hangnyoung Choi Hangnyoung Choi Jae Han Kim Hwiyoung Kim Hwiyoung Kim Keun-Ah Cheon Keun-Ah Cheon |
| author_facet | Hangnyoung Choi Hangnyoung Choi Jae Han Kim Hwiyoung Kim Hwiyoung Kim Keun-Ah Cheon Keun-Ah Cheon |
| author_sort | Hangnyoung Choi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T12:25:00Z |
| format | Article |
| id | doaj.art-b5b81a0799e648cbadee1df340b9dd4b |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-12T12:25:00Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-b5b81a0799e648cbadee1df340b9dd4b2023-08-30T01:41:09ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-08-011710.3389/fnins.2023.12291551229155Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning modelsHangnyoung Choi0Hangnyoung Choi1Jae Han Kim2Hwiyoung Kim3Hwiyoung Kim4Keun-Ah Cheon5Keun-Ah Cheon6Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Science in Medicine, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of KoreaYonsei University College of Medicine, Severance Hospital, Yonsei University Health System, Seoul, Republic of KoreaCenter of Clinical Imaging Data Science, Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Science in Medicine, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of KoreaIntroductionPrevious 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1229155/fullautism spectrum disorderparenting stressartificial intelligencemachine learningpredictor |
| spellingShingle | Hangnyoung Choi Hangnyoung Choi Jae Han Kim Hwiyoung Kim Hwiyoung Kim Keun-Ah Cheon Keun-Ah Cheon Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models Frontiers in Neuroscience autism spectrum disorder parenting stress artificial intelligence machine learning predictor |
| title | Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| title_full | Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| title_fullStr | Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| title_full_unstemmed | Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| title_short | Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| title_sort | identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models |
| topic | autism spectrum disorder parenting stress artificial intelligence machine learning predictor |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1229155/full |
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