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Detecting autism spectrum disorder using machine learning techniques An experimental analysis on toddler, child, adolescent and adult datasets

journal contribution
posted on 2021-01-01, 00:00 authored by Md Delowar Hossain, Muhammad Ashad Kabir, Adnan AnwarAdnan Anwar, Md Zahidul Islam
Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of toddler, child, adolescent and adult. We have evaluated state-of-the-art classification and feature selection techniques to determine the best performing classifier and feature set, respectively, for these four ASD datasets. Our experimental results show that multilayer perceptron (MLP) classifier outperforms among all other benchmark classification techniques and achieves 100% accuracy with minimal number of attributes for toddler, child, adolescent and adult datasets. We also identify that ‘relief F’ feature selection technique works best for all four ASD datasets to rank the most significant attributes.

History

Journal

Health Information Science and Systems

Volume

9

Issue

1

Article number

17

Pagination

1 - 13

Publisher

Springer

Location

Berlin, Germany

ISSN

2047-2501

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

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