Identifying Populations at High Risk for Occupational Back Injury with Neutral Networks

Details

• Personal Author:
  Arena VC ; Gardner LI ; Landsittel DP
• Description:
  For this study a simulation is conducted to investigate the accuracy of neural networks and logistic regression in identifying populations at high risk for occupational back injury. In contrast to most standard regression techniques, neural networks do not rely on linearity or explicitly specifying the nature of the association. Because the underlying relationships between work exposures, personal risk factors, and injury are often not well defined, neural networks may prove useful for injury risk assessment. Accuracy was assessed by comparing the injury status to the predicted level of risk in each worker. In simulations of a non-linear association, workers (used in the training data) were correctly classified 85% of the time with neural networks, 74% of the time with the main effects logistic model, and 79% of the time with the fully-specified logistic model. Using the test data, however, workers were correctly classified 67% of the time with neural networks, and 71% and 69% of the time with the main effects and fully-specified logistic models, respectively. Simulations of a null association indicated that neural networks may be more likely to overfit random associations. These findings provide a valuable guide concerning statistical methodology for identifying high-risk worker populations. [Description provided by NIOSH]
• Subjects:
  Musculoskeletal Diseases Occupational Health Risk Assessment Risk Factors Safety Workplace
• Keywords:
  Author Keywords: Classification Back-injuries Injuries Logistic Regression Models Musculoskeletal-system-disorders Occupational-health Risk-analysis Risk-factors Simulation-methods Simulation Study Statistical-analysis Work-environment Worker-health

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• ISSN:
  1080-7039
• Publisher:
  CRC Press Incorporated
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DSR - Division of Safety Research ; EID - Education and Information Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  4
• Issue:
  6
• NIOSHTIC Number:
  nn:20025317
• Citation:
  Hum Ecol Risk Assess 1998 Dec; 4(6):1337-1352
• Contact Point Address:
  National Institute for Occupational Safety and Health, 1095 Willowdale Road, M/S P1133, Morgantown, WV 26505
• Editor(s):
  Wassell JT; Bailer AJ
• Federal Fiscal Year:
  1999
• Peer Reviewed:
  True
• Source Full Name:
  Human and Ecological Risk Assessment
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:811e81a7f4ab16760852cf67cca8854292bac68d88808ce501d84a5d864dea7c6025a66bb860c0e1b5f8723063fd95b3ffdbc551e6823f501283d8ba38610fb6
• Download URL:
  https://stacks.cdc.gov/view/cdc/196753/cdc_196753_DS1.pdf
• File Type:
  [PDF - 225.07 KB ]