Early Detection of ADHD in Juveniles using recurrent neural networks

ADHD, or attention deficit and hyperactivity disorder, is a common problem among youngsters that falls under mental diseases. It is one of the most studied disorders, as many industrialized countries with advanced technology cannot diagnose it. In the land of Ethiopia, a study was carried out. The prevalence rate, which is the proportion of the population affected by diseases at a given period, is expected to be around 7 percent among youngsters. If preschool teachers or child care teachers understand what ADHD is and what research is being done on it, they can help the child and inform their parents about the child’s concerns. This would also benefit the research community because the data would be provided after the teachers read about the research. Furthermore, teachers can spot ADHD in its early stages, which can aid in early treatment. However, because not every teacher is aware of the study, various machine learning and deep learning techniques have been utilized to solve the current problem statement. The paper’s primary goal is to use a Recurrent Neural Network to detect ADHD at an early stage and increase the correctness of the problem statement.