Deep Forest in ADHD Data Classification

Abstract

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. INDEX TERMS Attention deficit hyperactivity disorder, classification, deep forest, fMRI. The associate editor coordinating the review of this manuscript and approving it for publication was Omid Kavehei.

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

“…Compared with deep learning, SVM, Logistic regression in image classification, face recognition, music classification and other fields, Zhou et al have proved the effectiveness of gcForest. GcForest has also been successfully widely studied in many fields such as medicine and social science [ 16 , 17 ].…”

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

“…These include k-fold cross validation (Qureshi and Lee, 2016 ; Eslami et al, 2019 ) (randomly splitting the data while maintaining class distribution for k times), re-sampling training set (Colby et al, 2012 ; Li X. et al, 2018 ) (under-sampling or over-sampling training set to have an even class distribution), and bootstrapping (Beare et al, 2017 ; Dekhil et al, 2018a ) (re-sampling the dataset randomly with replacement to oversample the dataset). One method for handling imbalanced data in ADHD, and ASD data sets is SMOTE which is used to oversample the minority class (Riaz et al, 2016 ; Farzi et al, 2017 ; Shao et al, 2019 ). SMOTE (Chawla et al, 2002 ) is a technique to adjust the class distribution of a data set, or to produce synthetic data for your ML model.…”

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

“…Eslami and Saeed [137] used a model based on a KNN classifier and also designed a model selection method to select the value of k for KNN. Shao et al [138] proposed an improved RF method that combines functional connectivity (FC) and low-frequency fluctuation amplitude (ALFF). Moreover, synthetic minority oversampling technology was exploited to generate minority ethnic group cascading feature samples, thus making the distribution of the sample data more balanced.…”