Deep Forest in ADHD Data Classification

Attention deficit hyperactivity disorder (ADHD) is a kind of mental disease which often appears among young children. Various machine learning techniques including deep neural networks have been used to classify ADHD. As an alternative of deep neural networks, the deep forest or gcForest recently proposed by Zhou and Feng has demonstrated excellent performance on many imaging tasks. Therefore, in this paper, we are going to investigate using fMRI data and gcForest to discriminate ADHD subjects against normal controls. Two types of features are extracted from the fMRI data, they are 1-D functional connectivity (FC) feature and 3-D amplitude of low frequency fluctuations (ALFF) feature. We propose a revised gcForest method which uses a combined multi-grained scanning structure to fuse the two features together, thus a new concatenated feature vector can be formed for each sample. Moreover, considering the imbalanced property of ADHD data, we utilize synthetic minority over-sampling technique combined with edited-nearest neighbor to form synthetic minority concatenated feature vector samples for data balancing. Finally cascade forest is used to take the concatenated feature vector samples as input for classification. We test our method on the ADHD-200 public data sets and evaluate its performance on the hold-out testing data. We compare our method with several methods in the literature. The experiment illustrates that our method performs better than the reported methods.