article information

Jundishapur Journal of Health Sciences: Vol.5, issue 2; 141-148
published online: June 21, 2013
article type: Research Article

Prediction of workers pulmonary disorder exposed to silica dust in stone crushing workshops using logistic regression and artificial neural networks techniques

authors:

avatar Maryam Farhadian 1 , avatar Hossien Mahjub , , avatar Mohsen Aliabadi 3 , avatar Saeed Musavi 1 , avatar Mehdi Jalali 3

Department of Biostatistics, School of public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran
Department of Occupational Health, School of public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran

how to cite: Farhadian M, Mahjub H, Aliabadi M, Musavi S, Jalali M. Prediction of workers pulmonary disorder exposed to silica dust in stone crushing workshops using logistic regression and artificial neural networks techniques. Jundishapur J Health Sci. 2013;5(2): 141-148. 

Abstract

The work exposure conditions such as dust concentration, exposure time, use of respiratory protection devices and smoking status are effective to cause pulmonary function disorder. The objective of this study was prediction of pulmonary disorders in workersexposed to silica dust using artificial neural networks and logistic regression.
A sample of 117 out of 150 workers employed inthe stone crushing workshops placed in Hamadan province, in the west of Iran, was selected based on simple random approach. Information about occupational exposure histories were collected using aquestionnaire. To assess the pulmonary disorder status in the workers exposed to silica dust based on the spirometry indices as well as the workers characteristics theprediction models of artificial neural networks and logistic regression were employed using the SPSS software version 16.
Measurements of pulmonary function indices of the studied workers showed that the indices for workers having pulmonary disorder versus the others were statistically significant (P <0.01). The results of the obtained models showed that the artificial neural networks and the logistic regression hada high performancefor prediction of pulmonary disorder status. However, the developed neural networks model had a better performance than the logistic regressionmodel in viewpoint ofsensitivity, specificity, kappa statistic and the area under ROC curve.
The neural networks prediction model was more accurate compared with the logistic regression. In this regards, the developed prediction model can be used as a helpful tool and guideline by occupational health experts for evaluating workers exposure conditions and determining the health priorities and control measures in the stone crushing workshops.

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