A comparative study on autism among children using machine learning classification

Ainie Hayati, Noruzman and Ngahzaifa, Ab Ghani and Nor Saradatul Akmar, Zulkifli (2022) A comparative study on autism among children using machine learning classification. In: Lecture Notes in Networks and Systems; International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2021 , 25-26 June 2021 , Al Buraimi. pp. 131-140., 322 (263669). ISSN 2367-3370 ISBN 978-303085989-3

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Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment that affects communication and behavior in humans. It is a condition associated with a complex brain disorder, leading to significant changes in a human being’s social interaction and behavior. Typically to detect toddlers who have ASD through screening tests is very expensive and time-consuming. Typically, detecting toddlers who have ASD through screening tests is very expensive and time-consuming. However, with machine learning technology today, autism can be diagnosed efficiency and accuracy. This study aims to analyze and make a comparison on which prediction model that gives a high accuracy after the feature selection. The importance of attributes is investigated using correlation and the predictive models are constructed for the detection of this disorder in children. The dataset consists of 1054 instances and each instance includes 19 attributes. Experimental results clearly show that using feature selection with 10 attributes can lead the impact of accuracy with predictive model of Random Forest (RF) returns the highest accuracy with 94.78%. The findings also indicated that the number of questions in screening tools can be reduced and give an impact with the good results.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Classification; Feature selection; Machine learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	13 Dec 2023 07:37
Last Modified:	13 Dec 2023 07:37
URI:	http://umpir.ump.edu.my/id/eprint/39641
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