Development of a Behavioral Health Stigma Measure and Application of Machine Learning for Classification.

Objective: Given the growing public health importance of measuring the change in mental health stigma over time, the goal of this study was to demonstrate the potential for using machine learning as a tool to analyze patterns of social stigma as a complement to traditional research methods.
Methods: A total of 1,904 participants were recruited through Sona Systems, Ltd (Tallinn, Estonia), an experiment management system for online research, to complete a self-reported survey. The collected data were used to develop a new measure of mental (behavioral) health stigma. To build a classification predictive model of stigma, a decision tree was used as the data mining tool, wherein a set of classification rules was generated and tested for its ability to examine the prevalence of stigma.
Results: A three-factor stigma model was supported and confirmed. Results indicate that the measure is content-valid and internally consistent. Performance evaluation of the machine learning-based classification algorithm revealed a sufficient inter-rater reliability with a predictive accuracy of 92.4 percent.
Conclusion: This study illustrates the potential for applying machine learning to derive a data-driven understanding of the extent to which stigma is prevalent in society. It establishes a framework for the development of an index to track stigma over time and to assist healthcare decision-makers with improving the health of populations and the experience of care for patients.