Differential diagnosis of autism spectrum disorder and global developmental delay based on machine learning and Children Neuropsychological and Behavioral Scale.
10.7499/j.issn.1008-8830.2306024
- Author:
Gang ZHOU
1
;
Xiao-Bin ZHANG
;
Xing-Da QU
1
;
Mei-Fang LUO
2
;
Qiong-Ling PENG
2
;
Li-Ya MA
2
;
Zhong ZHAO
1
Author Information
1. College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
2. Developmental-Behavioral Pediatrics, Shenzhen Bao'an Women's and Children's Hospital, Shenzhen, Guangdong 518102, China.
- Publication Type:Journal Article
- Keywords:
Autism spectrum disorder;
Child;
Children Neuropsychological and Behavioral Scale-Revision 2016;
Global developmental delay;
Machine learning
- MeSH:
Child;
Humans;
Autism Spectrum Disorder/psychology*;
Diagnosis, Differential;
Machine Learning;
Social Behavior
- From:
Chinese Journal of Contemporary Pediatrics
2023;25(10):1028-1033
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To investigate the efficacy and required indicators of Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) in the differential diagnosis of autism spectrum disorder (ASD) and global developmental delay (GDD).
METHODS:A total of 277 children with ASD and 415 children with GDD, aged 18-48 months, were enrolled as subjects. CNBS-R2016 was used to assess the developmental levels of six domains, i.e., gross motor, fine motor, adaptive ability, language, social behavior, and warning behavior, and a total of 13 indicators on intelligence age and developmental quotient (DQ) were obtained as the input features. Five commonly used machine learning classifiers were used for training to calculate the classification accuracy, sensitivity, and specificity of each classifier.
RESULTS:DQ of warning behavior was selected as the first feature in all five classifiers, and the use of this indicator alone had a classification accuracy of 78.90%. When the DQ of warning behavior was used in combination with the intelligence age of warning behavior, gross motor, and language, it had the highest classification accuracy of 86.71%.
CONCLUSIONS:Machine learning combined with CNBS-R2016 can effectively distinguish children with ASD from those with GDD. The DQ of warning behavior plays an important role in machine learning, and its combination with other features can improve classification accuracy, providing a basis for the efficient and accurate differential diagnosis of ASD and GDD in clinical practice.
