Research Area:  Machine Learning

Abstract:

Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data.

Keywords:  
brain
medical diagnostic computing
medical disorders
medical signal processing
neural net architecture
neurophysiology
pattern classification
support vector machines

Author(s) Name:  Muhammad Faiz Misman; Azurah A. Samah; Farah Aqila

Publisher name:  IEEE

DOI:  10.1109/AiDAS47888.2019.8970823

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8970823/