Automatic classification of hyperactive children: Comparing multiple artificial intelligence approaches

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using “Prtools”, we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research.

► An accurate classifier is proposed for distinguishing hyperactive children.
► Accurate diagnosis of behavioral disorders takes time and cost with high misdiagnosis.
► Artificial intelligence algorithms have high power in medical classifications.
► Different artificial intelligence approaches were examined.