Autism Screening Using Deep Embedding Representation

摘要

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. An early diagnosis of neurode-velopmental disorders can improve treatment and significantly decrease associated healthcare cost, which reveals an urgent need for the development of ASD screening. However, the data used for ASD screening is heterogenous and multi-source, resulting in existing screening tools for ASD screening are expensive, time-intensive and sometimes fall short in predictive accuracy. In this paper, we apply novel feature engineering and feature encoding techniques, along with a deep learning classifier for ASD screening. Algorithms were created via a robust deep learning classifier and deep embedding representation for categorical variables to diagnose ASD based on behavioral features and individual characteristics. The proposed algorithm is effective compared with baselines, achieving 99% sensitivity and 99% specificity. The results suggest that deep embedding representation learning is a reliable method for ASD screening.