title: Extreme learning machine-based classification of ADHD using brain structural MRI data.
abstract: BACKGROUND
  Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD.
  
  
  METHODS
  High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation.
  
  
  RESULTS
  We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular.
  
  
  CONCLUSIONS
  Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Biomedical Engineering Institute, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China ; National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an, People's Republic of China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Peng
authors.forename: Pan
authors.initials: P
authors.lastname: Lin
authors.forename: Tongsheng
authors.initials: T
authors.lastname: Zhang
authors.forename: Jue
authors.initials: J
authors.lastname: Wang
mesh_terms: D000293:Adolescent; D001185:Artificial Intelligence; D001289:Attention Deficit Disorder with Hyperactivity / Q000145:classification* / Q000175:diagnosis*; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D002648:Child; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012984:Software; D060388:Support Vector Machine