Constructing Interpretable Classifiers to Diagnose Gastric Cancer Based on Breath Tests

Procedia Computer Science 2017
Inese Poļaka, Evita Gašenko, Orna Barash, Hossam Haick, Mārcis Leja

Quick, inexpensive and accurate diagnosis of gastric cancer is a necessity, but at this moment the available methods do not hold up. One of the most promising possibilities is breath test analysis, which is quick, relatively inexpensive and comfortable to the person tested. However, this method has not yet been well explored. Therefore in this article the authors propose using transparent classification models to explain diagnostic patterns and knowledge, which is acquired in the process. The models are induced using decision tree classification algorithms and RIPPER algorithm for decision rule induction. The accuracy of these models is compared to neural network accuracy.

Atslēgas vārdi
Classification; Decision tree classifier; Classification rules; Cancer diagnostics; Breath analysis
DOI
10.1016/j.procs.2017.01.136
Hipersaite
http://www.sciencedirect.com/science/article/pii/S1877050917301370?via%3Dihub

Poļaka, I., Gašenko, E., Barash, O., Haick, H., Leja, M. Constructing Interpretable Classifiers to Diagnose Gastric Cancer Based on Breath Tests. Procedia Computer Science, 2017, Vol. 104, 279.-285.lpp. ISSN 1877-0509. Pieejams: doi:10.1016/j.procs.2017.01.136

Publikācijas valoda
English (en)