Predicting ADHD Subtypes Using Machine Learning on Brain Imaging Data

The use of machine learning approaches to differentiate between various types of Attention Deficit Hyperactivity Disorder (ADHD) using information from brain imaging. Our goal is to reduce the sensitivity of the ADHD subtype evaluation by using advanced neuroimaging technologies, such as structural and functional magnetic resonance imaging (MRI), in combination with intelligent machine learning algorithms. Our methodology involves preprocessing brain MRI data, point birth, and training colored machine literacy models to categorize various forms of attention-deficit/hyperactivity disorder (ADHD). The performance of these models is assessed within the parameters of this study using attributes that are similar to recall, perfection, and delicacy. According to preliminary research, machine literacy may effectively distinguish between various forms of attention-deficit/hyperactivity disorder (ADHD), which offers a foundation for more precise and well-established therapeutic approaches. In addition to assisting people in realizing their greatest potential, this approach clarifies the brain processes underlying attention-deficit/hyperactivity disorder (ADHD). Our results suggest that the individual procedure for attention-deficit/hyperactivity disorder (ADHD) could be rewritten by combining brain imaging data with machine literacy. This would lead to better clinical concerns and more tailored remedial actions.