Identification and Analysis of Neural Disorders Based on Hyperactivity and Spectrum Disorder with Functional MRI

A collection of conditions known as neurodevelopmental disorders affect a number of factors, including mood, learning capacity, self-control, and memory. The most frequent and prominent are attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The current diagnosis methods are time-consuming, human-mediated, and unreliable. This study focuses on the prediction of ADHD and ASD, and a comparative analysis is done using neural networks, support vector machine, gradient boosting, and logistic regression models to determine which performs well for the resting-state functional MRI (fMRI) data. During this procedure, time-series signals were collected from the brain region’s voxels and utilized to create functional connectivity characteristics. Our models were evaluated on various metrics, and we observed that a multi-layer perceptron model performs well.