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The Development of Work-related Musculoskeletal Disorders Prediction System

According to the International Labor Organization, 317 million accidents occurs on the work-related diseases and injuries each year(1). In addition, occupational diseases that showed a declining trend until 2011 are expected to rise again in 2013. Occupational disease, in the self-assessment, among the approximately 1.2 million workers, people who had experienced occupational diseases were judged to be ill due to their job or cause of the disease. Overseas, there is interest and high level research on the factors that affect occupational diseases. In Europe, there is a survey of occupational diseases in the European Union countries through the European Working Conditions Survey (EWCS), which measures the working environment and correlates various aspects, and research that contributes to European policy development, in particular the quality of work and employment issues. In Korea, the EWCS is supplemented with the contents of the question according to the characteristics of Koreans, and KWCS (Korean Working Conditions Survey) is being conducted for Koreans. In this system, KWCS data is used to analyze occupation of upper limb, lower back and lower limb. We have developed a probability prediction system that can cause musculoskeletal disorders. In this system, a decision tree, a logistic regression, and a neural network are selected as the data mining method for predicting the occurrence of detailed occupational diseases, and the decision tree algorithm which is the most reliable in the ROC curve is selected and applied.


Introduction
According to the International Labor Organization(ILO), 317 million of occupational accidents are happening around the world every year and are continuing to increase (1). In Korea, work-related musculoskeletal diseases tend to increase every year(2). In overseas countries, national survey is conducted to determine the factors attributable to these work-related diseases, such as European Working Conditions Survey(EWCS).
The 'Korean Working Conditions Survey' data used in this paper, is a survey data benchmarked to the EWCS based on the characteristics of Koreans(3). In Europe, EWCS data serves as a contribution to European policy development and employment issues through research and development, such as correlation analysis, and social interest in working environments between various aspects(4, 5).
In Korea, there are more studies on the predictive system of diseases occurrence probability than musculoskeletal disorders. There are more studies on predictive models than system development (7,8). In the case of other countries, studies are being conducted to develop a system for predicting the probability of musculoskeletal disorders, but it is still difficult to predict the overall probability of work-related musculoskeletal disorders by analyzing only one occupation(9, 10). Therefore, in this paper, the system was developed to analyze the actual users(physicians, occupational safety experts etc.) by applying the prediction algorithm of work-related musculoskeletal diseases, which was developed through preprocessing and analysis of KWCS data. Work-related musculoskeletal disorders prediction system is important for improving the working environment and prevent accidents, and to reduce costs incurred by industrial accidents.

Algorithm
To develop a predictive algorithm, the 3 rd KWCS data was used and 34,788 cases were selected by weighting a total of 50,032 data. The selected data were divided into upper limb, lower back and lower limb according to body parts. The analysis methodology was conducted by selecting Regression Analysis, Artificial Neural Network and Decision Tree which are used for predictive analysis. The algorithm used to develop the system was applied to the Decision Tree Analysis with the highest reliability in the ROC curve.
weighted A total of 50,032 data were weighted and 34,788 cases were selected and analyzed by Decision Tree. The data were analyzed by three segments, upper extremity, lower back and lower extremity depending of the body region. Also algorithm used to develop the system was applied to the Decision Tree model with the highest reliability in the ROC curve.  The tool used for the analysis was SAS E-Miner ver.14.1, which can be analyzed each analysis method as a node, and the result can be directly seen in the SAS code. Express were used as development tools. The development language used HTML5, Java script, JQuery, and ASP, The system applied a login system to limit users and prevent inadvertent data entry. This is because the accuracy of the data and the reliability of the results can be increase. Additionally, users can store information about patient or workers individually and all data are stored back into the database. These individual data are piled up to generate log data for the individual. All the log data can also be collected contributing to system reliability and improvement through algorithm improvement. We expect that we will be able to realize personalized medicine in the future.
The questionnaires applied to the system were selected form the KWCS questionnaire through variable importance analysis.

Results
The schematic diagram of the system is shown below. All users are required to register. When they sign up, they can pre-set individual codes, such as their number, on the server so that they can select their patients.
When the user logs in, the main menu is displayed. The main menu contains 'All Data', 'Past Results', and 'New Data'.

All Data tab
All data is divided into upper limb, lower back, and lower limb where users can see existing data stored in the entire DB.

Past Results tab
In this tab, users can see the results that they entered. In the case of hospital, the patients' results can be selected alone. In case of the workplace, the user can check the data of the worker entered. Also, one log data can be shown separately by inputting basic information of a worker.

New Data tab
New data can be input. Enter basic information of the patient or worker, or search for and select existing data, and start the survey.

Conclusion
The third data used in this research is difficult to confirm the results over time according to the characteristics of the survey data. In addition, since only the 3 rd data is analyzed, it is necessary to apply and analyze the latest data additionally. Therefore, we will analyze the total 1 st~3rd data, including 4 th data which is the latest, to develop an algorithm that can predict work-related musculoskeletal disorders, thereby developing algorithm for work-related diseases that can occur by work. In addition, studies on aging and musculoskeletal disorders are already underway in foreign countries. If the data of this system accumulates continuously, it is expected that it will be a stepping stone of research that can separate and predict disease and work-related diseases according to time flow through time series analysis. Furthermore, since it is difficult to see the result screen on the existing mobile web, we will develop a mobile application so that the users can understand the image in a bar graph format and display a brief explanation.
Finally, due to the characteristics of the system, in which personal information of patients and workers are entered, a thorough review of these issues will be needed.