Open AccessPredicting Autism Spectrum Disorder (ASD) for Toddlers and Children Using Data Mining Techniques
Author(s) -
Rasool Azeem Musa,
Mehdi Ebady Manaa,
Ghassan H. Abdul-Majeed
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1804/1/012089
Subject(s) - autism spectrum disorder , autism , naive bayes classifier , decision tree , autistic spectrum disorder , random forest , support vector machine , computer science , machine learning , artificial intelligence , psychology , correlation , data mining , developmental psychology , mathematics , geometry
Autism Spectrum Disorder (ASD) is a contemporary disease that has recently spread among toddlers and children. Many researchers have been interested to determine the features of the autism. However, this kind of studies is costly in term of the gathering information from several sources. In this paper, we introduced and applied a novel and early prediction techniques based on the using of data mining and machine learning tools. It is difficult to determine the features of any autism ages. In this paper, we used data mining predication techniques which play an integral role to predict the symptoms of autism for any age group. The data of this study is AQ-10 dataset which are involved for toddlers and children. The results present a superior performance for ASD classification. Random forest, Decision Tree, Support Vector Machine, and Naive Bayes the accuracy of 1.0 with the features selected by correlation technique.