Classification of ADHD subgroup with recursive feature elimination for structural brain MRI.

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.