Research Area:  Machine Learning

Abstract:

Autism Spectrum Disorder (ASD) is a well-known mental disorders that prevails in the ability of a persons social communication. The significance of early diagnosing drew the attention of researchers to use different machine learning-based procedures. Many analyses are done with the help of machine learning (ML) techniques to foresee meltdowns of autism together with Support Vector Machines, Random Forests, Naive Bayes, K-nearest Neighbors and many more. This paper gives a wide-spread review of papers applying machine learning in predicting ASD, along with algorithms for data analysis and classification. More than 80 research papers are considered, and the articles are assembled from the internet. Finally 48 research articles are coped up with the prerequisites in this study. The main goal of this review is to distinguish and mark out the machine learning trends in ASD literature and show the way to researchers curious in expanding the core of predicting ASD data and observe momentous research patterns in the field of ML. This paper will be a guideline to future researchers who are willing to work in the field of predicting ASD meltdown.

Keywords:  
Support vector machines
Autism
Silver
Machine learning algorithms
Mental disorders
Machine learning
Predictive models

Author(s) Name:  Sara Karim; Nazina Akter; Muhammed J. A. Patwary

Publisher name:  IEEE

DOI:  10.1109/ICEEICT53905.2021.9667827

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9667827