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 neu-ral 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 in-cludes 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 selec-tion was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 out-put neurons was employed. The efficiency of two models was compared by receiver operating characteris-tic analysis, root mean square and -2 Loglikelihood criteria.Results:The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regres-sion 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,respec-tively.Conclusions:Based on these three criteria,artificial neural network would give better performance than logis-tic regression.Although,the difference is statistically significant,it does not seem to be clinically signifi-cant.