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Finding Risk Factors for Long-Term Sickness Absence Using Classification Trees
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis a model for predicting if someone has an over-risk for long-term sickness absence during the forthcoming year is developed. The model is a classification tree that classifies objects as having high or low risk for long-term sickness absence based on their answers on the Health-Watch form. The HealthWatch form is a questionnaire about health consisting of eleven questions, such as "How do you feel right now?", "How did you sleep last night?", "How is your job satisfaction right now?" etc. As a measure on risk for long-term sickness absence, the Oldenburg Burnout Inventory and a scale for performance based self-esteem are used. Separate models are made for men and for women. The model for women shows good enough performance on a test set for being acceptable as a general model and can be used for prediction. Some conclusions can also be drawn from the additional information given by the classification tree; workload and work atmosphere do not seem to contribute a lot to an in-creased risk for long-term sickness absence, while job satisfaction seems to be one of the most important factors. The model for men performs poorly on a test set, and therefore it is not advisable to use it for prediction or to draw other conclusions from it.

Place, publisher, year, edition, pages
2013. , 32 p.
TRITA-MAT-E, 2013:51
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-131751OAI: diva2:658555
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Available from: 2013-10-22 Created: 2013-10-17 Last updated: 2013-10-22Bibliographically approved

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