Data Mining Techniques Applied to Hydrogen Lactose Breath Test

Abstract

n this work, we present the results of applying data mining techniques to hydrogen breath test data. Disposal of H2 gas is of utmost relevance to maintain efficient microbial fermentation processes.

Objectives Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production.

Methods Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients.

Results Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test.

Conclusions Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.