Machine Learning in ADHD and Depression Mental Health Diagnosis

Abstract

According to the World Health Organization (WHO) “depression” is considered a mental disorder. In this decade mental ailment like depression and stress has become common as well as a crucial public health concern and has a relevant impact on society. It influences the people of all age groups, male or female, urban or rural, educated or uneducated and even employed or unemployed. In this proposed work, a wearable is designed such that it captures the biological parameters experienced by the clinically depressed person while they undergo stress. IOT plays an important role in sensing, analysing and processing the data. This explores the current machine learning based methods used to identify Attention Deficit Hyperactivity Disorder (ADHD) and depression in humans. Prevalence of mental ADHD and depression is increasing worldwide, partly due to the devastating impact of the COVID-19 pandemic for the latter but also because of the increasing demand placed on the mental health services. It is known that depression is the most common mental health condition, affecting an estimated 19.7% of people aged over 16. ADHD is also a very prevalent mental health condition, affecting approximately 7.2% of all age groups, with this being conceived as a conservative estimate. We explore the use of machine learning to identify ADHD and depression using different wearable and non-wearable sensors/modalities for training and testing. With mental health awareness on the rise, it is necessary to survey the existing literature on ADHD and depression for a machine learning based reliable Artificial Intelligence (AI). With access to in-person clinics limited and a paradigm shift to remote consultations, there is a need for AI-based technology to support the healthcare bodies, particularly in developed countries