Predicting Mental Health through Machine Learning Algorithms

The significance of mental health in accomplishing the worldwide objectives for sustainable development has been the subject of research and analysis in recent years. Although predicting psychological disorders is a difficult undertaking involving numerous considerations, current research has accomplished significant progress in this field. Using data analytics and machine learning (ML) algorithms to examine patterns and find possible markers of mental health issues is one noteworthy method. Predicting mental illness is a complex task that involves various factors, and recent research has been making strides in this area. On notable approach involves the use of ML algorithms and data analytics to analyse patterns and identify potential indicators of mental health conditions. Mental illness in the workplace can stalk from various issues, and it's often the result of a combination of personal, organizational, and societal elements. Some common reasons: job related stress, poor effort life balance, absence of job security, bullying and harassment, organizational culture, poor management practices, lack of resources for mental health support, job dissatisfaction, discrimination etc… Personality disorders, anxiety disorders, phobias, psychotic-disorders, depression, mood-disorders, eating-disorders, and other conditions can all result from mental illness. Mental health in the Open Sourcing Mental Health (OSMI) tech survey dataset, employee mental health issues are explored in this paper for their underlying causes. Objective of this data analysis is to decrease the problems associated with mental illness by educating the public about mental illness in the workplace. This article advocates and advises on the reasons for extreme mental health practices to prevent undesirable events caused by numerous elements in the working environment. The COVID-19 pandemic has caused more than two years of uncertainty and uncertainty for people around the world. This study quantifies perceived age-based discrimination in the framework of the COVID-19 pandemic, as well as the detrimental impact of social isolation and loneliness, fear, and behavioural disturbances in older individuals based on perceptions of COVID-19 risks. The findings demonstrate that in the context of the COVID-19 pandemic, perceived ageism positively predicts loneliness and serves as an indirect predictor of mental illness. In addition, fear of Covid-19, social isolation and loneliness are good indicators of mental disorder.