Attention deficit hyperactivity disorder (ADHD) is a disease state of the mind which is frequently observed in young children. Different machine learning approaches, which include Deep Neural Networks (DNNs) and it helps in ADHD classification. The following have been recently proposed: ADHD to examine employing functional Magnetic Resonance Imaging (fMRI) information and gcForest to differentiate between ADHD and normal theme, cascade forest is employed to make use of the concatenated feature vector samples in the form of input for classification. But, classification accuracy takes large time consuming. In order to deal with this problem, Modified Cuckoo Search- Cascade Forest (MCS-CF) based feature selection algorithm is suggested which helps in the accuracy improvement of the classifier used in ADHD.

Attention Deficit Hyperactivity Disorder (ADHD), Functional Connectivity (FC), Amplitude of Low Frequency Fluctuations (ALFF), Modified Cuckoo Search (MCS), Cascade Forest (CF).