Network-Based Classification of ADHD Patients Using Discriminative Subnetwork Selection and Graph Kernel PCA

As one of the most prevalent behavioral disorders, attention deficit hyperactivity disorder (ADHD) is diagnosed in nearly 5% of children and 2-4% of adults [1,2]. For patients with ADHD, it is difficult to control their behaviors and focus their attentions, which adversely affect their social function and academic performance [3,4]. Meanwhile, nowadays diagnosis of ADHD is very challenging in clinic and the misdiagnose rate is usually high [5]. Therefore, developing more accurate and automatic diagnostic methods for ADHD is of great importance.

Source: Computerized Medical Imaging and Graphics - April 22, 2016 Category: Radiology Authors: Junqiang Du, Lipeng Wang, Biao Jie, Daoqiang Zhang Source Type: research