Modified Cuckoo Search-Cascade Forest (MCS-CF) for Attention Deficit Hyperactivity Disorder (ADHD) Diagnosis

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 exam...

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Bibliographic Details
Published inNeuroQuantology Vol. 18; no. 7; pp. 83 - 94
Main Authors Padmavathy, Dr.T.V., Vinothkumar, Dr.M., Vimal Kumar, Dr.M.N.
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 2020
Subjects
Accuracy
Artificial neural networks
Attention deficit hyperactivity disorder
Children
Classification
Decision trees
Feature extraction
Feature selection
Image classification
Machine learning
Magnetic resonance imaging
Preprocessing
Search algorithms
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Summary: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.
ISSN:1303-5150
1303-5150
DOI:10.14704/nq.2020.18.7.NQ20196