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.

Keywords

• Artificial neural network
• Logistic regression
• Low back pain
• Second national health survey