Prediction model for the return to work of workers with injuries in Hong Kong

Yanwen Xu, Che Hin Chan, Karen Hui Yu Ling Lo, Dan Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


This study attempts to formulate a prediction model of return to work for a group of workers who have been suffering from chronic pain and physical injury while also being out of work in Hong Kong. The study used Case-based Reasoning (CBR) method, and compared the result with the statistical method of logistic regression model. The database of the algorithm of CBR was composed of 67 cases who were also used in the logistic regression model. The testing cases were 32 participants who had a similar background and characteristics to those in the database. The methods of setting constraints and Euclidean distance metric were used in CBR to search the closest cases to the trial case based on the matrix. The usefulness of the algorithm was tested on 32 new participants, and the accuracy of predicting return to work outcomes was 62.5%, which was no better than the 71.2% accuracy derived from the logistic regression model. The results of the study would enable us to have a better understanding of the CBR applied in the field of occupational rehabilitation by comparing with the conventional regression analysis. The findings would also shed light on the development of relevant interventions for the return-to-work process of these workers.
Original languageEnglish
Pages (from-to)77-84
Number of pages8
Issue number1
Publication statusPublished - 1 Dec 2007


  • Case-based reasoning
  • Injured worker
  • Logistic regression
  • Return-to-work

ASJC Scopus subject areas

  • Rehabilitation
  • Public Health, Environmental and Occupational Health


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