Predicting the type of pregnancy using artificial neural networks and multinomial logistic regression: a comparison study

Authors:
Mehdi Sadat-Hashemi;Anoshirvan Kazemnejad;Caro Lucas;Kambiz Badie
Affiliations:
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modarres University, 14115-111, Tehran, Iran;Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modarres University, 14115-111, Tehran, Iran;Center of Excellence: Control and Intelligent Processing, ECE Department, Tehran University, and SIS, IPM, 14115-111, Tehran, Iran;Info-Society, Iran Telecom Research center, 14115-111, Tehran, Iran
Venue:
Neural Computing and Applications
Year:
2005

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Abstract

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using two different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression and a neural network based on the data and compared their results using three statistical indices: sensitivity, specificity and kappa coefficient. Based on these three indices, neural network proved to be a better fit for prediction on data in comparison to multinomial logistic regression. When the relations among variables are complex, one can use neural networks instead of multinomial logistic regression to predict the nominal response variables with several levels in order to gain more accurate predictions.