Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

Zhiyuan Sun; Yunhao Yuan; Xiaoxiao Dong; Zhimei Liu; Kelong Cai; Wei Cheng; Jingjing Wu; Zhiyuan Qiao; Aiguo Chen

doi:10.1016/j.ijchp.2023.100409

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

Int J Clin Health Psychol. 2023 Oct-Dec;23(4):100409. doi: 10.1016/j.ijchp.2023.100409. Epub 2023 Sep 7.

Authors

Zhiyuan Sun^{1

2}, Yunhao Yuan³, Xiaoxiao Dong^{1

2}, Zhimei Liu^{1

2}, Kelong Cai^{1

2}, Wei Cheng^{1

2}, Jingjing Wu^{1

2}, Zhiyuan Qiao^{1

2}, Aiguo Chen^{1

2

4}

Affiliations

¹ College of Physical Education, Yangzhou University, Yangzhou 225127, China.
² Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China.
³ School of Information Engineering, Yangzhou University, Yangzhou 225127, China.
⁴ Nanjing Institute of Physical Education, Nanjing 210014, China.

Abstract

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Keywords: Autism spectrum disorder; Exercise intervention; Outcomes; Prediction; Supervised machine learning.