comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey

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.