Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method

• Published in: Complementary therapies in clinical practice Vol. 54; p. 101825
• Main Authors: Xu, Keyun, Sun, Zhiyuan, Qiao, Zhiyuan, Chen, Aiguo
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2024
• Subjects: Autism severity; Diagnosis; Explainable machine learning; Gray matter volume; Physical fitness; Autism severity; Physical fitness; Diagnosis; Gray matter volume; Explainable machine learning
• ISSN: 1744-3881; 1873-6947; 1873-6947
• DOI: 10.1016/j.ctcp.2023.101825

This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity. •Autism severity is negatively correlated with muscular strength in ASD children.•Autism severity is positively correlated with GMV in specific brain regions.•Machine learning methods can effectively diagnose autism severity.