A Comparative Study of Machine Learning Techniques for Autism Spectrum Disorder (ASD) Detection
Date
2022
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Abstract
Autism Spectrum Disorder (ASD) is a developmental impairment caused by brain differences. ASD patients may have a recognized difference, such as a genetic disease [1]. Autism can be diagnosed at any age and is referred to as a "behavioral disorder" since symptoms often develop within the first two years of life [1]. In this study, we have implemented different classifier algorithms such as Decision Tree Classifier, Logistic Regression, and Multi-layer Perceptron classifier (MLP Classifier) for detecting ASD in three types of people - adults, adolescents, and children. The proposed techniques are evaluated on publicly available three different non-clinically ASD datasets [2]. After applying various machine learning techniques and handling missing values, results strongly suggest that ANN-based prediction models work better on all these datasets with higher accuracy of 98.58%, 98.30%, and 95.24% for Autistic Spectrum Disorder Screening Data for Adults, Children, and Adolescents, respectively.
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TECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
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Electrical and Computer Engineering
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North South University