ADHD Diagnosis using structural Brain MRI and Personal Characteristic Data with Machine Learning Framework

•Investigated cortical thickness-based and volumetric features as biomarkers to identify ADHD.•ADHD showed increased GM volume in fifteen brain regions while loss of cortical thickness in twenty-seven brain regions in comparison to Typical Developing Control (TDC).•Complementary information from structural magnetic resonance imaging and personal characteristic (PC) is also utilized.•An age-balanced and huge dataset of 316 ADHD and 316 TDC was created from publicly available dataset.•These biomarkers reached high classification accuracy of 75.00%. An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) without manual intervention. The present study emphasises utilising structural MRI and personal characteristic (PC) data for developing an automated diagnostic system for ADHD classification. Here, an age-balanced dataset of 316 ADHD and 316 Typically Developing Children (TDC) was prepared from the publicly available dataset. We extracted volumetric features from gray matter (GM) volumes from brain regions defined by Automated Anatomical Labelling (AAL3) atlas and cortical thickness-based (CT) features using the Destrieux atlas. A set of salient features were selected independently using minimum redundancy and maximum relevance (mRMR) and ensemble feature selection (EFS) methods. Decision models were trained using five well-known classifiers: K-nearest neighbours, logistic regression, linear Support Vector Machine (SVM), radial-based SVM (RBSVM), and Random Forest. The performance of the proposed system was evaluated using accuracy, recall, and specificity with ten runs of a ten-fold cross-validation scheme. We run seven experiments by considering different combinations of features. The maximum classification accuracy of 75% was obtained with CT and PC features with RBSVM and SVM with the EFS. An increase in GM volume in fifteen brain regions and loss of cortical thickness in twenty-seven brain regions were observed.