Neural network models to predict ulcerative colitis activity using standard clinico-biological parameters

Published: 3 Feb 2020 | Version 1 | DOI: 10.17632/gpsvsb563v.1
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Description of this data

R scripts for predicting ulcerative colitis endoscopic activity through standard clinico-biological parameters using three neural network models are found in the files provided.

First binary model to predict active/inactive endoscopic disease using seven categorical and 13 continuous input variables is built and tested in UCDiseaseActivity_1stNNModel.R script. Console outputs for this script are shown in UCDiseaseActivity_1stNNModel_ConsoleOutput.txt.

Second binary model to predict active/inactive endoscopic disease using 12 biological input variables is built and tested in UCDiseaseActivity_2ndNNModel.R script. Console outputs for this script are shown in UCDiseaseActivity_2ndNNModel_ConsoleOutput.txt.

The multiclass model to predict Mayo endoscopic score using seven categorical and 13 continuous input variables is built and tested in UCDiseaseActivity_3rdNNModel.R script. Console outputs for this script are shown in UCDiseaseActivity_3rdNNModel_ConsoleOutput.txt.

Experiment data files

Latest version

  • Version 1

    2020-02-03

    Published: 2020-02-03

    DOI: 10.17632/gpsvsb563v.1

    Cite this dataset

    Popa, Iolanda Valentina (2020), “Neural network models to predict ulcerative colitis activity using standard clinico-biological parameters”, Mendeley Data, v1 http://dx.doi.org/10.17632/gpsvsb563v.1

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Institutions

Universitatea de Medicina si Farmacie Gr T Popa Iasi Facultatea de Medicina

Categories

Artificial Intelligence, Artificial Neural Networks, Machine Learning, Ulcerative Colitis

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

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