Comparative study of machine learning algorithms for early autism spectrum disorder identification

Mingzhong Tang

doi:10.1117/12.2669421

24 March 2023 Comparative study of machine learning algorithms for early autism spectrum disorder identification

Mingzhong Tang

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126112X (2023) https://doi.org/10.1117/12.2669421
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

For decades, research scientists have utilized the newly developed machine learning and Artificial Intelligence technologies to treat complicated neurological disorders. Due to the growing trend of autism spectrum disorder (ASD), various research has been conducted in this field, using machine learning classifiers for early ASD identification. Compared with traditional ASD screening methods, new classification methods involving different machine learning algorithms have displayed significant advantages in accuracy, efficiency, and reliability. The choice of machine learning algorithms is important since different algorithms will have different performances under specific circumstances. In this study, resources of information from recent research in this field were carefully gathered. Various machine learning algorithms employed in the collected research have been thoroughly studied and summarized. The experimental data derived from such research, as well as the principle and function of these algorithms have been comprehensively analyzed. Advantages, disadvantages, and suggestion of the applied circumstances for the involved algorithms were also provided for further discussion.

Citation Download Citation

Mingzhong Tang "Comparative study of machine learning algorithms for early autism spectrum disorder identification", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126112X (24 March 2023); https://doi.org/10.1117/12.2669421

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available