@article{399,
author = "Maha F. Alajmi and Samir Abd-Elrazek and Hazem M. El-Bakry",
abstract = "Autism spectrum disorder (ASD), is a well-known mental disorder that affects a person's abilities for social communication. The importance of early diagnosis prompted researchers to use several machine learning-based techniques. With the aid of machine learning methods like Support Vector Machines, Random Forests, Naive Bayes, K-nearest Neighbors, and many more, several analyses are carried out to predict autism meltdowns. This study provides a comprehensive evaluation of studies using algorithms for data analysis and classification, as well as machine learning, to predict ASD. The surveys are compiled from the internet and consider more than 80 study studies. In the end, 41 research publications met the requirements for this investigation. The major objective of this study is to identify and separate the machine learning trends in the ASD literature, point out important research trends in the field of machine learning, and guide researchers interested in developing the fundamentals of predicting ASD data. This survey will serve as a manual for future scholars that are interested in the topic of foretelling ASD meltdowns.",
issn = "23942894",
journal = "IJASM",
keywords = "Autism Spectrum Disorder;Intelligent Information Systems;Machine Learning;Data Analysis and Classification",
month = "November",
number = "6",
pages = "94-107",
title = "{I}ntelligent {I}nformation {S}ystems for {P}redicting, {A}utism {D}isorder: {A} survey",
volume = "9",
year = "2022",
}