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Bioinformatics Advance Access published online on June 4, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth343
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received February 23, 2004
Revised May 13, 2004
Accepted May 13, 2004

Article

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

C. Baumgartner 1*, C. Böhm 2, D. Baumgartner 3, G. Marini 4, K. Weinberger 4, B. Olgemöller 5, B. Liebl 6, A. A. Roscher 7

1 Research Group for Biomedical Data Mining, University for Health Sciences, Medical Informatics and Technology, Innrain 98, A-6020 Innsbruck, Austria
2 Institute for Computer Science, University of Munich, Oettingenstrasse 67, D-80538 Munich, Germany
3 Department of Pediatrics, Medical University of Innsbruck, Anichstrasse 35, A-6020 Innsbruck, Austria
4 Biocrates Life Sciences Biotechnology GmbH, Innrain 66, A-6020 Innsbruck, Austria
5 Laboratory Becker, Olgemöller & Colleagues, Führichstrasse 70, D-81671 Munich, Germany
6 Public Health Newborn Screening Center of the State of Bavaria, Landesuntersuchungsamt Südbayern, D-85762 Oberschleissheim, Germany
7 Department of Biomedical Genetics and Molecular Biology, Dr. von Hauner Children's Hospital, University of Munich, Lindwurmstrasse 4, D-80337 Munich, Germany

* To whom correspondence should be addressed. E-mail: christian.baumgartner{at}umit.at.


  Abstract

Motivation: During the Bavarian newborn screening program all newborns have been tested for about 20 inherited metabolic disorders. Due 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, PKU and 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% vs. 16.2%, MCADD 88.4% vs. 54.6% compared to the established diagnostic markers). Our results clearly proof 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, Statistical software package ADE-4: http://pbil.univ-lyon1.fr/ADE-4.


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