Automated ML Approaches to Discriminate the Autism-Categorical Spectrum Disorder

Noora  Saleem Jumaah; Fouad A. Arif; Rasha Hani Salman

doi:10.58564/IJSER.2.2.2023.66

Authors

Noora Saleem Jumaah Ministry of Higher Education & Scientific Research, Iraq
Fouad A. Arif College of Engineering, Al-Iraqia University, Iraq
Rasha Hani Salman Department of Studies and Planning, Wasit University, Iraq

DOI:

https://doi.org/10.58564/IJSER.2.2.2023.66

Keywords:

Autism spectrum disorder (ASD), Machine learning (ML), Logistic Regression (LR), K-Nearest Neighbours (KNN)

Abstract

This disorder can be considered as neurodevelopmental which has an affection on an individual’s behavior, communicating as well as learning. Its diagnosis needs a lot of time and money. And if it is detected early, it is going to be very helpful via giving the infected ones the suitable treatment in good time. As the development of AI and ML, this disorder is able to be predicted very soon. Thus, the goal here can be the proposing of a system called ASD prediction system by utilizing Naïve bayes classifier for predicting if a person is infected with ASD or not to develop the screening method used to detect the illness. The experimental outcomes showed that the Naïve bayes classifier achieved a great precision, recall and F- measure of about (98. 975%, 98.972 % and 98 .972 %) in kid's dataset. On the other hand, it was noticed that the precision, recall and F- measure in the dataset of the teenagers can be approximately (98.136%, 98.076% and 98.067%) with an estimated error rate that is (0.044).

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