Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

• Published in: PloS one Vol. 11; no. 8; p. e0160697
• Main Authors: Qureshi, Muhammad Naveed Iqbal, Min, Beomjun, Jo, Hang Joon, Lee, Boreom
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.08.2016; Public Library of Science (PLoS)
• Subjects: Adolescent; Attention Deficit Disorder with Hyperactivity - classification; Attention Deficit Disorder with Hyperactivity - diagnosis; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention deficit hyperactivity disorder; Biology and Life Sciences; Brain; Brain - pathology; Child; Children; Classification; Classifiers; Comparative analysis; Computer and Information Sciences; Cortex; Cortex (frontal); Diagnosis; Diagnosis, Differential; Differential diagnosis; Female; Frontal lobe; Humans; Hyperactivity; Learning; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical diagnosis; Medical imaging; Medical research; Medicine and Health Sciences; Nervous system; Neural networks; Neuroimaging; Neuroimaging - methods; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Physical Sciences; Research and Analysis Methods; Software; Studies; Support Vector Machine; Support vector machines; Surface area; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0160697

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.