Comparative Analysis of Mental Health Prediction Using Machine Learning: A Study on Meditators and Non-Meditators

A day-by-day world evolving problems related to human beings are also increasing continuously. Among these issues, health-related concerns stand out as particularly significant. According to the World Health Organization in 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder, with depression and anxiety disorder being the most common. As a result of the COVID-19 pandemic in 2020, there was a significant increase in the number of people suffering from anxiety and depression. According to early projections, major depressive disorders and anxiety disorders will both rise by 26% and 28% in just one year, respectively. The early prediction of Mental health is very important to take Medicine This research article examines how effective machine learning algorithms are in predicting psychological symptoms in those who practice meditation compared to those who do not. Given the rising popularity of meditation as a self-care treatment, it is essential to understand its possible effects on psychological well-being. The research uses psychological symptom ratings and demographic data from both those who practice meditation and those who do not. Several machine learning algorithms are used to forecast psychiatric symptoms using variables like meditation habits, demographics, and other pertinent characteristics. Among the various algorithms, SVM and logistic regression performed very well and meditators showed high resilience towards anxiety, depression, and stress as compared to those who are not.