Comparison of Machine Learning Algorithms on Mental Health Survey Data

Document Type


Lead Author Type

MBI Masters Student


Dr. Guenter Tusch; tuschg@gvsu.edu

Embargo Period



OBJECTIVE: The objective of the study is to compare machine learning algorithms using mental health survey data and analyze the mental health condition of employees at the workplace (technology companies vs. non-technology companies).

Background: In currently developing high technology world the most global problem observed is mental illness. Its prevalence is critical and leads to major health outcomes. In this study, it was investigated if the work place (technology company and non-technology company) affects the mental health conditions of the employees. And models were built to predict the accuracy of the mental health conditions using machine learning techniques with an interactive visualization of the data.

METHODS: ‘R’ software models were used to develop and predict the analyses of mental health issues at work place. The techniques used were data mining and machine learning to analyze using Tree classifiers, Recursive Partitioning, Random Forest, Bagging, Artificial Neural Networks, Naive Bayes, and Support Vector Machine techniques. Tableau was used to develop the interactive and graphical view of the data.

RESULTS: I have found no difference between the employees who work at technology companies and non-technology companies using the statistical methods, in which a P value < 0.05 was considered significant. The model’s accuracy was ranging about 75% - 78% for a variety of models.

CONCLUSION: From this analysis, it was found that mental health conditions are not affected by their work place (technology companies and non-technology). The machine learning techniques used in this study tells us about the accuracy of the model’s predicted for mental health issues.

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