A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Chao Song; Zhong-Quan Jiang; Li-Fei Hu; Wen-Hao Li; Xiao-Lin Liu; Yan-Yan Wang; Wen-Yuan Jin; Zhi-Wei Zhu

doi:10.3389/fpsyt.2022.993077

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Front Psychiatry. 2022 Sep 21:13:993077. doi: 10.3389/fpsyt.2022.993077. eCollection 2022.

Authors

Chao Song¹, Zhong-Quan Jiang², Li-Fei Hu¹, Wen-Hao Li¹, Xiao-Lin Liu¹, Yan-Yan Wang¹, Wen-Yuan Jin¹, Zhi-Wei Zhu¹

Affiliations

¹ Department of Developmental and Behavioral Pediatrics, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China.
² School of Public Health, Lanzhou University, Lanzhou, China.

Abstract

Background: Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.

Method: From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).

Result: Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.

Conclusion: Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

Keywords: artificial intelligence; autism spectrum disorder; child; diagnostic model; intellectual disability; machine learning.