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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2005

01.09.2005 | Original Article

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

verfasst von: Seyed Mehdi Sadat-Hashemi, Anoshirvan Kazemnejad, Caro Lucas, Kambiz Badie

Erschienen in: Neural Computing and Applications | Ausgabe 3/2005

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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.
Vorheriger Artikel Adaptation of diagonal recurrent neural network model
Nächster Artikel Efficient vector quantization using genetic algorithm

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Metadaten
Titel
Predicting the type of pregnancy using artificial neural networks and multinomial logistic regression: a comparison study
verfasst von
Seyed Mehdi Sadat-Hashemi
Anoshirvan Kazemnejad
Caro Lucas
Kambiz Badie
Publikationsdatum
01.09.2005
Erschienen in
Neural Computing and Applications / Ausgabe 3/2005
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-004-0454-8

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