Machine learning approaches for mental health diagnosis

Abstract

Life expectancy may be greatly improved by accurately diagnosing mental health issues at an early stage. The goal of this study is to develop strong modelling tools for mental health clinical practise by using machine learning methods. An individual's risk of developing dementia, anxiety, depression, and other mental health issues will be assessed using machine learning techniques (e.g. genetics, cognition, demographics). Mental health problems may be treated more successfully when they are discovered early, which benefits both patients and the professionals who treat them. A person's psychological, emotional, and social well-being are all part of what is meant by mental health. It alters one's thoughts, feelings, and actions. From infancy and youth through maturity and beyond, good mental health is critical. The accuracy of five machine learning algorithms in detecting mental health disorders was examined in this research. Logistic regression, K-NN classifier, decision tree classifier, random forest, and stacking are the five machine learning approaches. We've tested these methods and applied them, and we've found the most accurate one out of all of them.