Classification of ADHD Patients Using Kernel Hierarchical Extreme Learning Machine
This work addresses the challenge of early and accurate ADHD diagnosis for patients, though it appears incremental as it builds on existing methods with specific improvements.
The study tackled the problem of diagnosing Attention-Deficit and Hyperactivity Disorder (ADHD) from brain imaging data by using a kernel hierarchical extreme learning machine to classify features based on brain functional connectivity dynamics, achieving superior classification rates compared to state-of-the-art models.
Recently, the application of deep learning models to diagnose neuropsychiatric diseases from brain imaging data has received more and more attention. However, in practice, exploring interactions in brain functional connectivity based on operational magnetic resonance imaging data is critical for studying mental illness. Since Attention-Deficit and Hyperactivity Disorder (ADHD) is a type of chronic disease that is very difficult to diagnose in the early stages, it is necessary to improve the diagnosis accuracy of such illness using machine learning models treating patients before the critical condition. In this study, we utilize the dynamics of brain functional connectivity to model features from medical imaging data, which can extract the differences in brain function interactions between Normal Control (NC) and ADHD. To meet that requirement, we employ the Bayesian connectivity change-point model to detect brain dynamics using the local binary encoding approach and kernel hierarchical extreme learning machine for classifying features. To verify our model, we experimented with it on several real-world children's datasets, and our results achieved superior classification rates compared to the state-of-the-art models.