#6025. Taxonomy-based data representation for data mining: an example of the magnitude of risk associated with H. pylori infection

July 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computational Mathematics;
Computational Theory and Mathematics;
Computer Science Applications;
Genetics;
Biochemistry;
Molecular Biology;
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Abstract:
Background: The amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data are not always necessary; more often, membership to a specific group (e.g. diet, social ‘bubble’, living area) is enough to build a successful machine learning or data mining model without overfitting it. Therefore, in this article we propose an approach to building taxonomies using clustering to replace detailed data from large heterogenous data sets from different sources, while improving interpretability. We used the GISTAR study data base that holds exhaustive self-assessment questionnaire data to demonstrate this approach in the task of differentiating between H. pylori positive and negative study participants, and assessing their potential risk factors. We have compared the results of taxonomy-based classification to the results of classification using raw data.
Keywords:
Classification; Data merging; Data representation; Heterogenous data; Taxonomy

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