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Currently submitted to: JMIR Pediatrics and Parenting

Date Submitted: Jan 11, 2020
(closed for review but you can still tweet)

Prediction Model for children's risk of autism spectrum disorder: a method based on deep learning artificial neural network

  • Luo Dan; 
  • Wang Zhuoxin; 
  • Sun Weiwei; 
  • Bian Zhiwei; 
  • Wang Fuzhi; 

ABSTRACT

Background:

Over the past two decades, the prevalence of autism spectrum disorder (ASD) among children worldwide has been rising rapidly. While Lab-based test were commonly used in the clinical diagnosis of autism, it is not feasible to test all new-borns for autism.

Objective:

This study focused on the risk factors of children with autism in the pre-pregnancy, early gestational (first month to six month) and perinatal stages as proposed by clinical epidemiological studies, and applied a deep learning artificial neural network to establish an early warning model of children with autism.

Methods:

A multivariate questionnaire on risk factors for autism in children was developed in this study. Parents of children with (n = 137) and without autism (n = 186) in five Chinese cities were investigated. The data were split into two completely independent datasets: training set (80%) and test set (20%). The sensitivity, specificity, and accuracy of four risk factor set (RFS) models were compared. The AUCs of four prediction models were also compared.

Results:

The sensitivity and accuracy values of the RFS-B model were superior to those of the other three models. The specificity of the RFS-C was superior to that of the other three models. The AUCs of the four RFS models were computed to be 0.876, 0.905, 0.850 and 0.870.

Conclusions:

The results of the present study indicate that the deep learning artificial neural network has potential value in early risk prediction for children with autism.


 Citation

Please cite as:

Dan L, Zhuoxin W, Weiwei S, Zhiwei B, Fuzhi W

Prediction Model for children's risk of autism spectrum disorder: a method based on deep learning artificial neural network

JMIR Preprints. 11/01/2020:17772

DOI: 10.2196/17772

URL: https://preprints.jmir.org/preprint/17772

Per the author's request the PDF is not available.