Purpose: Study of the classification offered by the artificial neural networks (ANNs) for the “Patient Information” variable in the “Not” category in all data groups: Training, Testing and Holdout.

Methods: This study collects data from hospitals in the Burgos University Hospital, Spain, for two years, configuring a data file with 647 cases and 9 variables, 7 of them referred to the attitude to Informed consent, Sex and Age. We perform a descriptive analysis in order to have information about the variables that make up the classification/prediction model (Artificial Neural Network), how the data are distributed by category (“Yes” and “Not”) of the “Patient Information” variable.

Results: The structure of the most efficient artificial neural network found in the classification of the categories of the “Patient Information” variable (“Yes” and “Not” categories) is the binomial Hidden layer-Output layer: Hyperbolic tangent-Softmax Dependent variable: (“Patient Information”; Partition: Training 60%, Testing 20% and Holdout 20%).

Conclusions: The classification/prediction of the “Patient Information” variable by means of the artificial neural network, perceptron, offers us the low classification/prediction of the “Not” category, which is object of this study. One of the factors is due to the few data available for the three phases. Another factor is that the “Person to be informed” variable influences differently depending on the category. An experimental study shows that the classification of the “Not” category improves when a new covariant variable, for example “Consultation time” is introduced into the network.

informed consent, patient information, crosstabs and artificial neural network.

Received: February 3, 2023; Accepted: March 15, 2023; Published: March 28, 2023

How to cite this article: Elena Martín Pérez, Jacobo Salvat Dávila and Quintín Martín Martín, Study of the classification of the “not” category on informed consent through artificial neural networks, JP Journal of Biostatistics 23(2) (2023), 95-106. http://dx.doi.org/10.17654/0973514323006

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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