Comparison of logistic regression and neural network-based classifiers for bacterial growth Original Research Article

In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Linear and nonlinear logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and probabilistic neural networks (PNN) based classifiers were developed and compared in relation to their accuracy in classification of bacterial growth/no–growth data pertaining to pathogenic Escherichia coli R31 as affected by temperature and water activity. The comparisons between the four developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification contingency matrices. The ANN-based classifiers outperformed the logistic regression based counterparts. Within the same group, the PNN-based classifier was more accurate than the FEBANN-based classifier, and the nonlinear logistic regression-based classifier was more accurate than the linear one. The optimal PNN-based classifier was a perfect classifier with 100% growth detection accuracy and zero false alarm rate. The advantages and limitations pertaining to the development of the various classifiers were discussed.