Machine Learning Based, A Real-Time Analysis and Prediction of Mental Health Disorders

Sarvendra Kumar Pankaj, Dr. Vijay Dhote

Abstract: Real-time analysis and prediction of mental health disorders using machine learning is an area of growing interest in the healthcare industry. With the increasing prevalence of mental health disorders, there is a need for accurate and timely diagnosis and treatment to improve patient outcomes. Machine learning algorithms can be trained on large datasets of patient information, including symptoms, medical history, and demographic data, to identify patterns and predict the likelihood of different mental health disorders. Mental health is the aggregation of emotional, social and psychological well-being of a person. It effects on the person?s thinking, acting and feeling capability. Mental health is a measure of handling stress and decision making with every step-in life. There is so much data available that we are now able to compile data for mental health professionals by applying this approach they will benefit to clinicians the opportunity to personalize the professional & able to perform their job in better way in. Machine learning algorithms could help determine key behavioural biomarkers to aid mental health professionals in deciding if a patient is at risk of developing a particular mental health disorder. Additionally, the algorithms may assist in tracking effectiveness of a treatment plan. This paper reviews about the application of ML to mental health prediction, which includes a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions.

Keywords: Machine learning, Appetite, Mental illness, Depression, Schizophrenia