6533b82bfe1ef96bd128e0a5
RESEARCH PRODUCT
Constructing Interpretable Classifiers to Diagnose Gastric Cancer Based on Breath Tests
Evita GaenkoHossam HaickOrna BarashInese PolakaMarcis Lejasubject
Artificial neural networkComputer sciencebusiness.industryDecision treePattern recognition02 engineering and technologyDecision rule021001 nanoscience & nanotechnologyMachine learningcomputer.software_genre03 medical and health sciencesStatistical classification0302 clinical medicine030220 oncology & carcinogenesisGeneral Earth and Planetary SciencesArtificial intelligence0210 nano-technologybusinesscomputerGeneral Environmental Sciencedescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 | Procedia Computer Science |