Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey

This paper 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. These modalities include functional Magnetic Resonance Imagery (fMRI), Electroencephalography (EEG), Medical Notes, Video and Speech. 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.