A Machine Learning Way to Classify Autism Spectrum Disorder
DOI:
https://doi.org/10.3991/ijet.v16i06.19559Keywords:
ASD, ML, SVM, KNN, RF, NB, SGD, AdaBoost, CN2, AQ, CAAbstract
In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.
Downloads
Published
How to Cite
Issue
Section
License
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.