ARTIFICIAL NEURAL NETWORKS BASED-PREDICTION OF AUTISM SPECTRUM DISORDER

Abstract

Aim: Autism Spectrum Disorders (ASD) is one of the important neurodevelopmental disorders. This study aimed to perform artificial-intelligence-based modeling based on the prenatal-perinatal factors, family history, and developmental characteristics, which are emphasized as risk factors for ASD in the literature. Materials and Methods: The study was designed with a retrospective management and data from 136 children with ASD and 143 healthy children were included. Results: According to the findings of the MLP model, the five most important factors were the mean age of first words (months), the mean age of head control (months), the mean age of sitting without support (months), history of autism in the family, and the mean paternal age at pregnancy (years), respectively. Overall percentages of the training and testing samples were 91.4% and 88.0%. AUC for the model was 0.922 for the separation of the autism and control groups. Conclusion:The proposed model is able to successfully differentiate patients with autism spectrum disorders from healthy individuals and identify factors associated with the disease.

Keywords

• artificial neural networks
• autism
• prenatal risk factors
• perinatal risk factors

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