MATEC Web Conf.
Volume 125, 201721st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|Number of page(s)||6|
|Published online||04 October 2017|
Evaluation of diagnostic classifiers using artificial clinical cases
Institute of Computer and Information Systems, Military University of Technology, Warsaw, Poland
* Corresponding author: email@example.com
Evaluation of classifiers in diagnosis support systems is a non-trivial task. It can be done in a form of controlled and blinded clinical trial, which is often difficult and costly. We propose a new method for generating artificial medical cases from a knowledge base, utilizing the concept of so-called medical diamonds. Cases generated using this method have features analogous to that of double-blinded trial and, thus, can be used for measuring sensitivity and specificity of diagnostic classifiers. This is easy and low-cost method of evaluation and comparison of classifiers in diagnosis support systems. We demonstrate that this method is able to produce valuable results when used for evaluation of similarity-based classifiers as well as shallow and deep neural networks.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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