Modelling of classification rules on metabolic patterns including machine learning and expert knowledge

Authors:
Christian Baumgartner;Christian Böhm;Daniela Baumgartner
Affiliations:
Research Group for Biomedical Data Mining, Institute for Information Systems, University for Health Sciences, Medical Informatics and Technology, Innrain 98, A-6020 Innsbruck, Austria;Institute for Computer Science, University of Munich, Oettingenstrasse 67, D-80538 Munich, Germany;Department of Pediatrics, Innsbruck Medical University, Anichstrasse 35, A-6020 Innsbruck, Austria
Venue:
Journal of Biomedical Informatics
Year:
2005

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Abstract

Machine learning has a great potential to mine potential markers from high-dimensional metabolic data without any a priori knowledge. Exemplarily, we investigated metabolic patterns of three severe metabolic disorders, PAHD, MCADD, and 3-MCCD, on which we constructed classification models for disease screening and diagnosis using a decision tree paradigm and logistic regression analysis (LRA). For the LRA model-building process we assessed the relevance of established diagnostic flags, which have been developed from the biochemical knowledge of newborn metabolism, and compared the models' error rates with those of the decision tree classifier. Both approaches yielded comparable classification accuracy in terms of sensitivity (