A Review on Machine Learning Approaches in Diagnosis of ADHD Based on Big Data

With increased interest in public health and apprehension in the overall development of child growth and adolescence, the demand for the identification of Attention Deficit Hyperactivity Disorder (ADHD) is a present-day concern of research. Inattentiveness, hyperactivity, and impulsivity are some of the signs of ADHD, a neurodevelopmental disease, and its identification is done using symptom surveys, clinical interviews, and neuropsychological testing. ADHD is noticeable as early as 3 to 12 years of a child and if left untreated leads to low self-esteem; disturbed relationships; poor performance in education and workplaces; and higher-risk stress-related repercussions. Treatment typically involves medications and behavioral interventions. The challenging aspect in ADHD diagnosis is it resembles the signs of other diseases including obesity and compulsive gambling. Several deep learning models diagnosis the huge population registry data into individuals with ADHD accurately using big data analytics (BDA), such predictions can be made years prior to age of the onset and the behavioral risks factors of childhood and adolescence can be monitored. Deep learning models can be used by professionals in the relevant fields who comprehend the classification's motivations performing better for big and varied data sets. This chapter summarizes the different ADHD diagnosis methods using deep learning techniques. Attention deficit hyperactivity disorder (ADHD) is an acute medical issue that appears in children, but symptoms persist in adolescence if not treated. An ADHD person's brain and its activities are different compared to a healthy person. The person with ADHD loses interest in activities, develops low self-esteem, has disturbed relationships, poor performance in education and workplaces, over-activity, and lack of self-control. This chapter summarizes the different ADHD diagnosis methods using deep learning techniques. ADHD symptoms are distinguished into three major subtypes. ADHD diagnosis is based on various factors such as magnetic resonance imaging analysis; physiological electroencephalogram (EEG) signals; questionnaires/rating scales; game simulation; motion activity measures like actigraphy and accelerometer; and miscellaneous ways like pupillometric data, Twitter data analysis, magnetoencephalography and genetic features. The chapter discusses recent works and experiments using machine learning/deep learning models that use questionnaires/rating scales