International Journal of Engineering and Technology

Childhood autism is one of the most common developmental disorders in children, whose disabilities generally tend to follow through adulthood. The therapy given to an autistic child depends on its accurate assessment, which is a challenging clinical decision making problem. This paper describes the application of functional fuzzy model in autism assessment support systems. The fuzzy model uses Takagi-Sugeno Kang (TSK) type of inference mechanism for supporting the assessment. A rule based single input-single output first order functional fuzzy model has been applied to a set of real world data and the experimental results were compared with the conventional autism assessment method. The results demonstrate that the proposed method works better in putting the borderline between autistic grades in terms of accuracy. Thus the functional fuzzy model can support in assessing the grade of childhood autism with certainty.