A Job Stress Predictive Model Evaluation Through Classifier's Algorithms

Nowadays, job stress is very common and it has a high cost in terms of workers' health, absenteeism and lower performance. Although stress is not a disease, it is the first sign of a bigger problem which can generate long-term damages. This paper presents a predictive model of job stress which was obtained from data collected by telephone mobile and sensors. Relevant attributes were identified through a correlation analysis. Learning algorithms were applied in order to determine the predictive model. We use the classifier algorithms ZeroR, Naive Bayes, Simple Logistics, Support Vector Machine, k-Nearest-Neighbor, AdaBoost and Random Tree. The proposed model obtained an accuracy of 0.947, a coverage of 0.941 and an F-measure of 0.939. This model was implemented in a mobile application called “TestStress”. Also, the results obtained of the experimentation with the app are presented.