J Korean Med Sci.  2018 May;33(19):e144. 10.3346/jkms.2018.33.e144.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

• Lee J
• Kim HR

• KMID: 2410529
• DOI: http://doi.org/10.3346/jkms.2018.33.e144

Abstract

BACKGROUND
Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period.
METHODS
An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared.
RESULTS
The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent.
CONCLUSION
It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy.

Keyword

Return to Work; Accidents, Occupational; Machine Learning

MeSH Terms

Accidents, Occupational*
Dataset
Decision Trees
Forests
Insurance
Logistic Models
Machine Learning*
Odds Ratio
Return to Work
Support Vector Machine
Workers' Compensation