Work related low back disorders (LBDs) continue to pose significant occupational health problem that affects the quality of life of the industrial population. The main objective of this study was to explore the application of various data mining techniques, including neural networks, logistic regression, decision trees, memory-based reasoning, and the ensemble model, for classification of industrial jobs with respect to the risk of work-related LBDs. The results from extensive computer simulations using a 10-fold cross validation showed that memory-based reasoning and ensemble models were the best in the overall classification accuracy. The decision tree and memory-based reasoning models were the most accurate in classifying jobs with high risk of LBDs, whereas neural networks and logistic regression were the best in classifying jobs with low risk of LBDs. The decision tree model delivered the most stable results across 10 generations of different data sets randomly chosen for training, validation, and testing. The classification results generated by the decision tree were the easiest to interpret because they were given in the form of simple 'if-then' rules. These results produced by the decision tree method showed that the peak moment had the highest predictive power of LBDs.
Keywords
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting;
Author Keywords: low back disorders; assessment of lifting jobs; knowledge discovery; data mining techniques
Contact
Waldemar Karwowski, Center for Industrial Ergonomics, University of Louisville, Lutz Hall, Room 445, Louisville, KY 40292
Links with this icon indicate that you are leaving the CDC website.
The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
You will be subject to the destination website's privacy policy when you follow the link.
CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.
For more information on CDC's web notification policies, see Website Disclaimers.
CDC.gov Privacy Settings
We take your privacy seriously. You can review and change the way we collect information below.
These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.
Cookies used to make website functionality more relevant to you. These cookies perform functions like remembering presentation options or choices and, in some cases, delivery of web content that based on self-identified area of interests.
Cookies used to track the effectiveness of CDC public health campaigns through clickthrough data.
Cookies used to enable you to share pages and content that you find interesting on CDC.gov through third party social networking and other websites. These cookies may also be used for advertising purposes by these third parties.
Thank you for taking the time to confirm your preferences. If you need to go back and make any changes, you can always do so by going to our Privacy Policy page.