Detection of Attention Deficit/Hyperactivity Disorder (ADHD)

Abstract

Abstract: In recent years, Machine Learning has had a huge impact on a range of technological disciplines. As technology expands, machine learning provides an exciting opportunity in health care to help medical professionals care for patients and manage clinical data. It improves the accuracy of diagnoses, personalize health care, and find novel solutions to decades-old problems. Hospitals and health care companies have begun to recognize the ability of machine learning to improve decisionmaking and reduce risk in the medical field, which has led to several new and exciting career opportunities. Attention Deficit/Hyperactivity Disorder (ADHD) is a chronic condition generally characterized by hyperactivity, impulsiveness, and difficulty in paying attention. The early diagnosis of ADHD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. At present, there are two main methods available - using fMRI data and tracking eye movements. The proposed approach offers a method to assist with the ADHD diagnosis with an emphasis on young children who are still in the early stages of development. It tries to discover the eye-tracking patterns of ADHD using machine learning. The fundamental concept is to convert eye-tracking scan-paths into a visual representation, thus the diagnosis may be viewed as an image classification task.