Application of Artificial Neural Network Models in Occupational Safety and Health Utilizing Ordinal Variables.
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| Title: | Application of Artificial Neural Network Models in Occupational Safety and Health Utilizing Ordinal Variables. |
|---|---|
| Authors: | Moayed, Farman A.1 farman.moayed@indstate.edu, Shell, Richard L.2 |
| Source: | Annals of Occupational Hygiene. Mar2011, Vol. 55 Issue 2, p132-142. 11p. |
| Subjects: | Artificial neural networks, Industrial safety, Occupational disease risk factors, Safety engineers, Quantitative research, Logistic regression analysis, Chi-squared test, Statistical correlation, Industrial hygiene |
| Abstract: | Safety professionals and practitioners are always searching for methods to accurately assess the association between exposures and possible occupational disorders or diseases and predict the outcome of any variable. Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used today. Artificial neural network (ANN) models are another method of predicting outcomes, which are gradually finding their way into the safety field. Limited studies have shown that they are capable of predicting outcomes more accurately than LR, but they have been tested either on continuous or on dichotomous variables or combinations of them. The objective of this research was to demonstrate that ANN models can perform better than LR models with data sets comprised of all ordinal variables, which has not been done so far. The data set used in this research was collected from construction workers using the Work Compatibility questionnaire. The data set contained only ordinal variables both as input (exposure) and as output (outcome) variables. LR models and ANN models were constructed using the same data set and the performance of all models was compared by using the log-likelihood ratio. The result of this study showed that ANN models performed significantly better than LR models with a data set of all ordinal variables as well as other types of variables such as dichotomous and continuous. [ABSTRACT FROM PUBLISHER] |
| Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 58150043 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Artificial Neural Network Models in Occupational Safety and Health Utilizing Ordinal Variables. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moayed%2C+Farman+A%2E%22">Moayed, Farman A.</searchLink><relatesTo>1</relatesTo><i> farman.moayed@indstate.edu</i><br /><searchLink fieldCode="AR" term="%22Shell%2C+Richard+L%2E%22">Shell, Richard L.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Occupational+Hygiene%22">Annals of Occupational Hygiene</searchLink>. Mar2011, Vol. 55 Issue 2, p132-142. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+safety%22">Industrial safety</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+disease+risk+factors%22">Occupational disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Safety+engineers%22">Safety engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+hygiene%22">Industrial hygiene</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Safety professionals and practitioners are always searching for methods to accurately assess the association between exposures and possible occupational disorders or diseases and predict the outcome of any variable. Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used today. Artificial neural network (ANN) models are another method of predicting outcomes, which are gradually finding their way into the safety field. Limited studies have shown that they are capable of predicting outcomes more accurately than LR, but they have been tested either on continuous or on dichotomous variables or combinations of them. The objective of this research was to demonstrate that ANN models can perform better than LR models with data sets comprised of all ordinal variables, which has not been done so far. The data set used in this research was collected from construction workers using the Work Compatibility questionnaire. The data set contained only ordinal variables both as input (exposure) and as output (outcome) variables. LR models and ANN models were constructed using the same data set and the performance of all models was compared by using the log-likelihood ratio. The result of this study showed that ANN models performed significantly better than LR models with a data set of all ordinal variables as well as other types of variables such as dichotomous and continuous. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/annhyg/meq079 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 132 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Industrial safety Type: general – SubjectFull: Occupational disease risk factors Type: general – SubjectFull: Safety engineers Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Chi-squared test Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Industrial hygiene Type: general Titles: – TitleFull: Application of Artificial Neural Network Models in Occupational Safety and Health Utilizing Ordinal Variables. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moayed, Farman A. – PersonEntity: Name: NameFull: Shell, Richard L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 00034878 Numbering: – Type: volume Value: 55 – Type: issue Value: 2 Titles: – TitleFull: Annals of Occupational Hygiene Type: main |
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