Limits...
Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods.

Farhadian M, Aliabadi M, Darvishi E - Indian J Occup Environ Med (2015 May-Aug)

Bottom Line: Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran.

ABSTRACT

Background: Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers.

Materials and methods: A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.

Results: Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.

Conclusion: Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

No MeSH data available.


Related in: MedlinePlus

Receiver operating characteristics curve for the test phase of developing logistic regression
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4596076&req=5

Figure 1: Receiver operating characteristics curve for the test phase of developing logistic regression

Mentions: To compare the performance of the developed models to predict the grades of hearing loss (Class 1: Normal, Class 2: Slight, Class 3: Moderate), (ROC) curves were also plotted. As mentioned that the higher ROC areas indicating the better performance of the models. ROC curves for the test phases of developing ANN and multinomial logistic regression models were shown in Figures 1 and 2. The results confirmed that the areas under the ROC curves were significantly greater for the ANN than logistic regression.


Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods.

Farhadian M, Aliabadi M, Darvishi E - Indian J Occup Environ Med (2015 May-Aug)

Receiver operating characteristics curve for the test phase of developing logistic regression
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596076&req=5

Figure 1: Receiver operating characteristics curve for the test phase of developing logistic regression
Mentions: To compare the performance of the developed models to predict the grades of hearing loss (Class 1: Normal, Class 2: Slight, Class 3: Moderate), (ROC) curves were also plotted. As mentioned that the higher ROC areas indicating the better performance of the models. ROC curves for the test phases of developing ANN and multinomial logistic regression models were shown in Figures 1 and 2. The results confirmed that the areas under the ROC curves were significantly greater for the ANN than logistic regression.

Bottom Line: Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran.

ABSTRACT

Background: Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers.

Materials and methods: A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.

Results: Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.

Conclusion: Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

No MeSH data available.


Related in: MedlinePlus