Building a Low Back Injury Risk Classifier Using Evolutionary Computation-Reference-Cited by-同舟云学术

Building a Low Back Injury Risk Classifier Using Evolutionary Computation

Published:1998-10 Issue:12 Volume:42 Page:881-885
ISSN:2169-5067
Container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting
language:en
Short-container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Author:

Carnahan Brian J.¹,Redfern Mark S.¹

Affiliation:

1. Department of Industrial Engineering The University of Pittsburgh 1048 Benedum Hall Pittsburgh, Pa. 15261

Abstract

Injury risk models can play a key role in ergonomic worksite analysis directed at preventing low back disorders. Such models can be used to classify lifting tasks as having the same characteristics as those tasks which have had a high (or low) incidence rate of back injuries. Two evolutionary computation techniques (genetic algorithms GA, and genetic programming GP) were used to construct low back injury risk models. A GA model, GP model, logistic regression model, and an artificial neural network were constructed and tested using 235 documented lifting task cases. Results indicated that the evolutionary approaches were superior to the other models in terms of classification performance and parsimony.

Publisher

SAGE Publications

Subject

General Medicine,General Chemistry

Link

http://journals.sagepub.com/doi/pdf/10.1177/154193129804201209

Reference11 articles.

1. Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley Publishers, New York, pp. 2–7.

2. Hennekens C. H., Buring J. E. (1987) Epidemiology in Medicine, Little Brown and Company, Boston, MA. pp. 327–347.

3. Adaptation in Natural and Artificial Systems

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