A Data Mining Based Approach to Predict Autism Spectrum Disorder Considering Behavioral Attributes
Autism Spectrum Disorder (ASD) is a disease connected to the growth of brain. It affects a person's way of communication and behavior. It impacts a person's way of understanding and social attachments. Moreover, people with ASD experience various types of symptoms, for examples, difficulty while interacting with others, repetitive behaviors, and face difficulty to function properly in other areas of day-to-day life. These symptoms generally occur in early childhood. Most of the people are not well aware of the syndrome, therefore, they do not know whether a person is suffering from the disorder or not. As a result, instead of helping which very often leads the victim to become isolated from society. However, the prediction of the disorder at an early stage should help to get a quick recovery. In this paper, the Random Forest classifier was used for the prediction of ASD based on behavioral attributes. We are satisfied with the results with the overall accuracy of 0.96%. The dataset used for prediction had 10 behavioral attributes and 10 more individual attributes.
Autism Spectrum Disorder, Random Forest, Decision Tree, Prediction, Classification, Data Mining