Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children. We designed a model selection scheme called J-Eros which is able to pick the optimum value of k for k-Nearest-Neighbor from the training data …