title: Classification of ADHD subgroup with recursive feature elimination for structural brain MRI.
abstract: This article reports the binary classification results of ADHD patients among three subgroups by using ADHD-200 dataset. We have proposed a modified feature selection approach using standard RFE-SVM model. Our results show the significance of the proposed method by making a comparison of J-statistics, F1-score and classification accuracy based on the feature selection from the original RFE-SVM vs. the proposed modification of RFE-SVM. In addition, we have also compared the number of features in each setting to achieve the highest accuracy. After ten-fold cross-validation, we have achieved 84.17% accuracy using a linear SVM classifier. Moreover, we have found significant anatomical regions that can serve as a potential biomarker for the ADHD subgroups classification.
authors.forename: Muhammad Naveed Iqbal
authors.initials: MN
authors.lastname: Qureshi
authors.lastname: Boreom Lee
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000145:classification*; D015415:Biomarkers; D001921:Brain; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*
chemical_list: D015415:Biomarkers