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

ABSTRACT:

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

SUBMITTER: Bozorgmehr A 

PROVIDER: S-EPMC8096078 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature