Machine-learning classification of ADHD with biomarkers of cerebral cortical thickness: parcellation schemes, gender effects and feature selection

In this study, we attempt to use machine-learning algorithms for ADHD classification with cerebral cortical thickness. We compared three cortical parcellation schemes and three different sets of features. The results supported the usage of Aparc and A2009s of FreeSurfer and suggested that recursive feature elimination effectively increased the predication accuracies. In addition, gender is an influential feature for the classification.