New perspectives on deep neural networks in decision support in surgery

Konstantin Savenkov, Vladimir Gorbachenko and Anatoly Solomakha

International Journal of Data Mining, Modelling and Management, 2021, vol. 13, issue 4, 317-336

Abstract: The paper considers the development of a neural network system for predicting complications after acute appendicitis operations. A neural network of deep architecture has been developed. As a learning set, a set developed by the authors based on real clinic data was used. To select significant features, a method for selecting features based on the interquartile range of the F1-score is proposed. For preliminary processing of training data, it is proposed to use an overcomplete autoencoder. Overcomplete autoencoder converts the selected features into a space of higher dimension, which, according to Cover's theorem facilitates the classification of features according to complication and not corresponding to complication. To overcome the overfitting of the network, the dropout method of neurons was used. The neural network is implemented using the Keras and TensorFlow libraries. Trained neural network showed high diagnostic metrics on test data set.

Keywords: neural networks; features selection; learning neural networks; overfitting; overcomplete autoencoder; medical diagnostics. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=119628 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:13:y:2021:i:4:p:317-336

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().