A Review on Predicting Autism Spectrum Disorder(ASD) meltdown using Machine Learning Algorithms

• Published in: 2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) pp. 1 - 6
• Main Authors: Karim, Sara, Akter, Nazina, Patwary, Muhammed J. A., Islam, Md. Rashedul
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18-11-2021
• Subjects: ASD Meltdown; Autism; Autism spectrum disorder(ASD); Machine learning; Machine learning algorithms; Machine learning model; Mental disorders; Predictive models; Silver; Support vector machines
• DOI: 10.1109/ICEEICT53905.2021.9667827

Autism Spectrum Disorder (ASD) is a well-known mental disorders that prevails in the ability of a person's 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.