Mental health treatment prediction using machine learning

Today’s working IT professionals frequently struggle with stress issues. Because of shifting lifestyles and working environments, stress among employees is becoming more likely. The problem is still out of control even though many businesses and organizations offer mental health-related programme an...

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Bibliographic Details
Main Authors: Sai Koushik, P. Satya, Raj, Harshit, Nalini, S.
Format: Conference Proceeding
Language:English
Subjects:
Colleges & universities
Data acquisition
Decision trees
Employee benefits
Machine learning
Mental health
Psychological stress
Stress
Working conditions
Workplaces
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Summary:Today’s working IT professionals frequently struggle with stress issues. Because of shifting lifestyles and working environments, stress among employees is becoming more likely. The problem is still out of control even though many businesses and organizations offer mental health-related programme and seek to improve the workplace. In this study, we’ll study the stress patterns of working individuals and find the factors that have the biggest effects on their stress levels using machine learning approaches. Data from working professionals in the tech industry’s replies to the OSMI mental health survey from 2017 was taken into consideration in this regard. Our model was trained using a number of machine learning techniques following meticulous data cleaning and preparation. The preceding information is true models was acquired and comparison analysis was done. Among the models used, Xg-boost gave the most accurate results. Using Decision Trees, it was discovered that Gender, family history, and health benefits availability at work were all major stress-influencing variables. With the help of these findings, businesses may now focus on findings may now focus on finding to make their employees workplaces less stressful and more pleasant. Public target groups including high school kids, college students, and working professionals are employed in this identification procedure.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0217198