Fuzzy data mining for autism classification of children

Abstract

Autism is a development condition linked with healthcare costs, therefore, early screening of autism symptoms can cut down on these costs. The autism screening process involves presenting a series of questions for parents, caregivers, and family members to answer on behalf of the child to determine the potential of autistic traits. Often existing autism screening tools, such as the Autism Quotient (AQ), involve many questions, in addition to careful design of the questions, which makes the autism screening process lengthy. One potential solution to improve the efficiency and accuracy of screening is the adaptation of fuzzy rule in data mining. Fuzzy rules can be extracted automatically from past controls and cases to form a screening classification system. This system can then be utilized to forecast whether individuals have any autistic traits instead of relying on the conventional domain expert rules. This paper evaluates fuzzy rule-based data mining for forecasting autistic symptoms of children to address the aforementioned problem. Empirical results demonstrate high performance of the fuzzy data mining model in regard to predictive accuracy and sensitivity rates and surprisingly lower than expected specificity rates when compared with other rule-based data mining models.