Open Access
ARTICLE
Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of Children with Autism and ADHD
Mahiye Uluyagmur Ozturka, Ayse Rodopman Armanb, Gresa Carkaxhiu Bulutc, Onur Tugce Poyraz Findikb, Sultan Seval Yilmazd, Herdem Aslan Gencb, M. Yanki Yazgane,f, Umut Tekera, Zehra Cataltepea
a Computer Engineering Department Istanbul Technical University Istanbul, Turkey;
b Marmara University Medical Faculty Child and Adolescent Psychiatry, Istanbul, Turkey;
c Mus State Hospital Child and Adolescent Psychiatry, Mus, Turkey;
d TC Health Municipality Medeniyet University Child and Adolescent Psychiatry, Istanbul Turkey;
e Güzel Günler Polyclinics, Istanbul, Turkey;
f Yale Child Study Center, New Haven, CT, USA
* Corresponding Author: Mahiye Uluyagmur Ozturk,
Intelligent Automation & Soft Computing 2018, 24(4), 891-905. https://doi.org/10.31209/2018.100000058
Abstract
Emotion recognition behavior and performance may vary between people with major
neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit
Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences
for early diagnosis and individual treatment purposes. This study represents a
methodology by using statistical data analysis and machine learning to provide help to
psychiatrists and therapists on the diagnosis and individualized treatment of participants
with ASD and ADHD. In this paper we propose an emotion recognition experiment
environment and collect eye tracker fixation data together with the application log data
(APL). In order to detect the diagnosis of the participant we used classification algorithms
with the Tomek links noise removing method. The highest classification accuracy results
were reported as 86.36% for ASD vs. Control, 81.82% for ADHD vs. Control and 70.83%
for ASD vs. ADHD. This study provides evidence that fixation and APL data have
distinguishing features for the diagnosis of ASD and ADHD.
Keywords
Cite This Article
M. U. Ozturk, A. R. Arman, G. C. Bulut, O. T. P. Findik, S. S. Yilmaz
et al., "Statistical analysis and multimodal classification on noisy eye tracker and application log data of children with autism and adhd,"
Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 891–905, 2018. https://doi.org/10.31209/2018.100000058