Supervised machine learning techniques for the classification of metabolic disorders in newborns

Authors:
C. Baumgartner;C. Böhm;D. Baumgartner;G. Marini;K. Weinberger;B. Olgemöller;B. Liebl;A. A. Roscher
Affiliations:
Research Group for Biomedical Data Mining, 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,;Biocrates Life Sciences Biotechnology GmbH, Innrain 66, A-6020 Innsbruck, Austria,;Biocrates Life Sciences Biotechnology GmbH, Innrain 66, A-6020 Innsbruck, Austria,;Laboratory Becker, Olgemöller & Colleagues, Füührichstrasse 70, D-81671 Munich, Germany,;Public Health Newborn Screening Center of the State of Bavaria, Landesuntersuchungsamt Südbayern, D-85762 Oberschleissheim, Germany;Department of Biomedical Genetics and Molecular Biology, Dr von Hauner Children's Hospital, University of Munich, Lindwurmstrasse 4, D-80337 Munich, Germany
Venue:
Bioinformatics
Year:
2004

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Abstract

Motivation: During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. Results: Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity 96.8%, specificity 99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism. Availability: WEKA machine learning package: www.cs.waikato.ac.nz/~ml/weka and statistical software package ADE-4: http://pbil.univ-lyon1.fr/ADE-4