0000000000681208
AUTHOR
Orna Barash
Constructing Interpretable Classifiers to Diagnose Gastric Cancer Based on Breath Tests
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.
Associations of diet and lifestyle factors with common volatile organic compounds in exhaled breath of average-risk individuals.
Background Detection of diseases via exhaled breath remains an attractive idea despite persisting gaps in understanding the origin of volatile organic compounds (VOCs) and their relationship with the disease of interest. Data on factors potentially influencing the results of breath analysis remain rather sparse and often controversial. In this study, we aimed to investigate the associations of common VOCs in exhaled breath of average-risk individuals with socio-demographic and lifestyle factors, medical conditions as well as diet. Methods Alveolar breath samples of 1447 men and women were collected in the morning after fasting and were analyzed using gas-chromatography linked with mass-spec…