An Intelligent Technique to Predict the Autism Spectrum Disorder Using Big Data Platform

Jaber A. Alwidian

Modern Applied Science, 2023, vol. 17, issue 1, 28

Abstract: Autism or autism spectrum disorder (ASD) is considered a psychiatric disorder. It is a condition that puts constraints on the use of linguistic, cognitive, communicative, and social skills and abilities. Recently, many data mining techniques have been developed to help autism patients by discovering the main features of the condition and the correlation between them. In this paper, we employ the association classification (AC) technique as a data mining approach to predict whether or not an individual has an autism. The Intelligent Classification Based on Association rules (ICBA) algorithm is proposed for finding the correlations between the features to decide whether an individual has autism in its early stage, especially in childhood. The ICBA algorithm incorporates the chi-square method to select the best feature to make the decision, in addition to proposing new techniques in all phases and increasing number of folds to 2size of data/10. The proposed algorithm is compared against four well-known AC algorithms in terms of accuracy to evaluate their behavior in the prediction task using big data platform. The results show a better performance for the ICBA algorithm in most experiments. Moreover, all of the considered algorithms had an increased level of accuracy when the chi-square method was used.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/0/0/48579/52298 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/0/48579 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:17:y:2023:i:1:p:28

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().