JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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STUDY OF THE CLASSIFICATION OF THE “NOT” CATEGORY ON INFORMED CONSENT THROUGH ARTIFICIAL NEURAL NETWORKS

Authors

  • Elena Martín Pérez
  • Jacobo Salvat Dávila
  • Quintín Martín Martín

Keywords:

informed consent, patient information, crosstabs and artificial neural network

DOI:

https://doi.org/10.17654/0973514323006

Abstract

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.

Received: February 3, 2023
Accepted: March 15, 2023

References

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Published

2023-03-28

Issue

Vol. 23 No. 2 (2023)

Section

Articles

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How to Cite

STUDY OF THE CLASSIFICATION OF THE “NOT” CATEGORY ON INFORMED CONSENT THROUGH ARTIFICIAL NEURAL NETWORKS. (2023). JP Journal of Biostatistics, 23(2), 95-106. https://doi.org/10.17654/0973514323006

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