Year: 2024
Type: article
Abstract: Mental health concerns in the technology sector have become increasingly prominent, drawing attention due to the industry's high-pressure environment and intense workloads. This study investigates the application of machine learning techniques to predict mental health conditions among technology industry professionals. The research utilizes a comprehensive dataset, analyzing factors such as work-life balance, job satisfaction, workplace environment, and individual well-being. The study implements and evaluates several Machine Learning approaches, including Logistic Regression, Random Forest, Decision Tree Classifier, KNN, Gradient Boosting Classifier, XGB Classifier, to determine the most effective method for predicting mental health outcomes. The results highlight the potential of predictive analytics in early identification, enabling organizations to implement proactive strategies to enhance mental wellness and improve employee productivity. Additionally, this investigation emphasizes the necessity of incorporating mental health support mechanisms in technology-driven workplaces to foster a more positive work environment. The paper concludes with a discussion of the study's limitations and potential avenues for improving mental health prediction models in the future.
Author: Mansi Bhonsle
Institution: MIT Art, Design and Technology University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4404419188