Title of Abstract
Exploring the Perception of Mental Health in the Tech Industry through Data Analysis
Session Title
Mental Health and General Wellness
College
College of Arts and Sciences
Department
Mathematics
Faculty Mentor
Kristen Abernathy, Ph.D.
Abstract
Mental illness is one of the leading causes of death in The United States. Similarly, mental illness costs millions of dollars in direct and indirect expenses, not to mention the impact mental illness has on productivity. When one’s mental illness is not treated effectively, there are negative consequences for the individual and for the general population as well. The stigma around mental illness compounds the problem. This research focuses on the perception of mental health in the workplace, specifically in the tech industry. We have taken 5 years of survey data from Open Source Mental Illness (OSMI) to implement several machine learning algorithms like “K-nearest Neighbors” and “Decision Trees” in order to discover the leading variables in how mental health is perceived. The models in this paper will give predictions for an individual’s perception of mental health based on answers to a selection of prompting questions. These predictions note the most important markers and will influence the creation of solutions for resolving the stigma around mental health.
Honors Thesis Committee
Kristen Abernathy, Ph.D.; Michael Lipscomb, Ph.D.; Arran Hamm, Ph.D.; Zachary Abernathy, Ph.D.
Course Assignment
HONR 450H - Abernathy & HONR 451H - Lipscomb
Type of Presentation
Oral presentation
Exploring the Perception of Mental Health in the Tech Industry through Data Analysis
Mental illness is one of the leading causes of death in The United States. Similarly, mental illness costs millions of dollars in direct and indirect expenses, not to mention the impact mental illness has on productivity. When one’s mental illness is not treated effectively, there are negative consequences for the individual and for the general population as well. The stigma around mental illness compounds the problem. This research focuses on the perception of mental health in the workplace, specifically in the tech industry. We have taken 5 years of survey data from Open Source Mental Illness (OSMI) to implement several machine learning algorithms like “K-nearest Neighbors” and “Decision Trees” in order to discover the leading variables in how mental health is perceived. The models in this paper will give predictions for an individual’s perception of mental health based on answers to a selection of prompting questions. These predictions note the most important markers and will influence the creation of solutions for resolving the stigma around mental health.
