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Position Papers of the 19th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 40

Relative performance of Neural Networks and Binary Logistic Regression in a Variable Selection framework

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DOI: http://dx.doi.org/10.15439/2024F8304

Citation: Position Papers of the 19th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 40, pages 25–32 ()

Full text

Abstract. In this study, the predictive abilities of a binary response variable on a set of descriptors using Multilayer Perceptron neural networks and binary logistic regression in a variable selection context were evaluated. The data used was related to the identification of prenatal factors linked to premature birth in women already in labor. The stepwise selection method on binary logistic regression and the Olden selection method based on the neural network approach were used to select the most relevant variables to predict the probability of premature birth by women. Then, the two selection methods were combined with binary logistic regression and multilayer perceptron neural network models. Using the performance criteria such as sensitivity, precision, rate of good classification, F-score and Area Under the Curve (AUC), the selection methods were compared in order to choose the best model. It appears from the analysis that the best procedure for selecting variables in a binary variable prediction is the use of the Stepwise procedure followed by multilayer perceptron neural networks.

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