Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.

Bozorgmehr, Arezoo; Thielmann, Anika; Weltermann, Birgitta

BACKGROUND Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure. METHODS We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors', and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the 'operating area under the curve' (AUC), sensitivity, and positive predictive value. FINDINGS Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490-0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684-0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605-0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634-0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556-0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders. CONCLUSIONS Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.