Articles

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

Abstract

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neu­ral network with a logistic regression in prediction of low back pain.
Methods: Data from the second national health survey were considered in this investigation. This data in­cludes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selec­tion was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 out­put neurons was employed. The efficiency of two models was compared by receiver operating characteris­tic analysis, root mean square and -2 Loglikelihood criteria.
Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regres­sion was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respec­tively.
Conclusions: Based on these three criteria, artificial neural network would give better performance than logis­tic regression. Although, the difference is statistically significant, it does not seem to be clinically signifi­cant.

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IssueVol 41 No 6 (2012) QRcode
SectionArticles
Keywords
Artificial neural network Logistic regression Low back pain Second national health survey

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H. Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey. Iran J Public Health. 1;41(6):86-92.