A DNN-based diagnosis on autism spectrum disorder in children
- 1 Software and Engineering, Xi'an Jiaotong University
* Author to whom correspondence should be addressed.
Abstract
The prevalence of autism spectrum disorder (ASD) witnesses a sharp increasing in recent years, and early diagnosis and intervention of ASD are critically needed. This study explored the efficacy of Deep Neural Networks (DNN) in diagnosing ASD among children aged 0 to 10. Utilizing the latest dataset derived from the ASDTests mobile application, which encompasses behavioral characteristics of over 2,000 children, we implemented a DNN model to capture complex non-linear patterns indicative of ASD. The results of comparative analysis with traditional machine learning models revealed DNN's superior accuracy in predicting ASD, indicating that the DNN achieved a significant improvement in identifying minority classes post-imbalance learning treatment. The promising results, including the 99.55% accuracy rate, paved the way for future investigations into integrating DNN with multimodal data analysis and other advanced algorithms to enhance early diagnostic processes and intervention strategies for ASD.
Keywords
Deep Neural Networks, Deep Learning, Autism Spectrum Disorder, Machine Learning, Diagnosis
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Cite this article
Tan,S. (2024). A DNN-based diagnosis on autism spectrum disorder in children . Applied and Computational Engineering,67,13-20.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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