Classifying and Predicting Occupational Incident Severity

dc.contributor.author Freeman, Steven
dc.contributor.author Kakhki, Fatemeh
dc.contributor.author Freeman, Steven
dc.contributor.author Mosher, Gretchen
dc.contributor.author Mosher, Gretchen
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2020-05-06T00:18:34.000
dc.date.accessioned 2020-06-29T22:35:38Z
dc.date.available 2020-06-29T22:35:38Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2016-01-01
dc.date.issued 2017-01-01
dc.description.abstract <p>The focus of the study is to build a predictive model and assess its performance in classifying the financial severity of occupational injuries in agribusiness bulk storage facilities (i.e. grain elevators). The data specifically look at food processing and feed milling operations within these facilities. The severity of occupational injuries is determined by the total dollar amount incurred on medical costs, indemnity costs and other expenses in workers’ compensation claims. The data is available from an agribusiness insurance provider in Midwest USA. First, the most important independent variables that affect the total cost of claims are extracted from the original dataset. The claims cost variables are then applied as input factors in constructing a classification decision tree and random forests trees with the claims classified as severe and non-severe. Claims over ten thousand dollars are considered severe while those with zero to ten thousand dollars are classified as non-severe. For the purpose of balancing model overfitting and prediction accuracy, the data is partitioned to training, validation and test sets. The results show that the decision tree and random forests trees have accuracy rates of 94% and 93% respectively in predicting that a future claim will be classifies as severe or non-severe based on characteristics of the injury. In addition, incident location and injured worker demographics do not have a significant effect on predicting claim severity. The presented model identifies higher injury risk groups and prevalent causes of incidents in work environments, allowing a more focused intervention effort in the food processing and feed milling sectors. In addition, it is applicable in forecasting cost of future claims and identifying factors that contribute to escalation of claims costs.</p>
dc.description.comments <p>This proceeding is published as Kakhki, Fatemeh Davoudi, Steven A. Freeman, and Gretchen A. Mosher. "Classifying and Predicting Occupational Incident Severity." 2017 ATMAE Annual Conference, Cincinnati, OH, Nov. 1-3, 2017. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/590/
dc.identifier.articleid 1591
dc.identifier.contextkey 17639239
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/590
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/638
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/590/0-ATMAE_Conference_PermissionGrant.pdf|||Sat Jan 15 01:03:44 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/590/2017_FreemanSteven_ClassifyingPredicting.pdf|||Sat Jan 15 01:03:45 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Classification Decision Tree
dc.subject.keywords Random Forests Trees
dc.subject.keywords Occupational Injuries
dc.title Classifying and Predicting Occupational Incident Severity
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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relation.isAuthorOfPublication d4270080-adb0-4e32-aa5c-6bce7f39e6a8
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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