An accessible and efficient autism screening method for behavioural data and predictive analyses. (December 2019)

• Record Type: Journal Article
• Authors: Thabtah, Fadi
• Is Part Of: Health informatics journal. Volume 25:Number 4(2019)
• Page Start: 1739
• Page End: 1755
• Publication Date: 2019-12
• Subjects: accessibility -- autism spectrum disorder screening methods -- autism spectrum disorder -- behavioural science -- classification -- machine learning -- mobile application; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458218796636
• Languages: English
• ISSNs: 1460-4582

Abstract

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic procedures are time-consuming and not cost-effective. Overall, the economic impact of autism and the increase in the number of autism spectrum disorder cases across the world reveal an urgent need for the development of easily implemented and effective screening methods. This article proposes a new mobile application to overcome the problem by offering users and the health community a friendly, time-efficient and accessible mobile-based autism spectrum disorder screening tool called ASDTests. The proposed ASDTests app can be used by health professionals to assist their practice or to inform individuals whether they should pursue formal clinical diagnosis. Unlike existing autism screening apps being tested, the proposed app covers a larger audience since it contains four different tests, one each for toddlers, children, adolescents and adults as well as being available in 11 different languages. More importantly, the proposed app is a vital tool for data collection related to autism spectrum disorder for toddlers, children, adolescent and adults since initially over 1400 instances of cases and controls have been collected. Feature and predictive analyses demonstrate small groups of autistic traits improving the efficiency and accuracy of screening processes. In addition, classifiers derived using machine learning algorithms report promising results with respect to sensitivity, specificity and accuracy rates.