Classification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder

Authors

  • Azurah A Samah Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Siti Nurul Aqilah Ahmad Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Hairudin Abdul Majid Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Zuraini Ali Shah Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Haslina Hashim Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Nuraina Syaza Azman Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Nur Sabrina Azmi Artificial Intelligence and Bioinformatics Group (AIBIG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MALAYSIA
  • Dewi Nasien Department of Information Technology, Universitas Riau, Pekanbaru, Indonesia

DOI:

https://doi.org/10.11113/ijic.v11n2.352

Keywords:

Functional Magnetic Resonance Imaging (fMRI), Variational Autoencoder (VAE), Attention Deficit Hyperactivity Disorder (ADHD), Independent Component Analysis (ICA), Nilearn

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.

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Published

2021-10-31

How to Cite

A Samah, A. ., Ahmad, S. N. A. ., Abdul Majid, H. ., Ali Shah, Z. ., Hashim, H. ., Azman, N. S. ., … Nasien, D. . (2021). Classification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder. International Journal of Innovative Computing, 11(2), 81–87. https://doi.org/10.11113/ijic.v11n2.352

Issue

Section

Computer Science