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

Citation on Big Data Computing: Advances in Technologies, Methodologies, and Applications (Computational Intelligence Techniques)

Chapter 15: A Review on Machine Learning Approaches in Diagnosis of ADHD Based on Big Data

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. These effects become very difficult for the person to pay concentration, to listen and follow instructions with thoughtful attention, and stay calm, leading to bad effects on relationships, academic performance, and employment. Hence, people struggle more in performing their daily activities with major symptoms characterized into inattention, hyperactivity, and impulsivity. In this regard, ADHD etiology is a challenge, where it is characterized as neuropsychiatric conditions that affect children and adolescents. ADHD diagnosis, treatment that clinicians can practice, causes and occurrences about the disorder from the caregiver, and interventions as researchers are a major part of the study and are explained in the latter part of this chapter.