Modelling of classification rules on metabolic patterns including machine learning and expert knowledge.
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 (>95.2%), while the LRA models built on flags showed significantly enhanced specificity. The number of false positive cases did not exceed 0.001%.
Baumgartner
Christian
C
Research Group for Biomedical Data Mining, Institute for Information Systems, University for Health Sciences, Medical Informatics and Technology, Innrain 98, A-6020 Innsbruck, Austria. christian.baumgartner@umit.at
Böhm
Christian
C
Baumgartner
Daniela
D
D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000378:metabolism; D016000:Cluster Analysis; D003198:Computer Simulation; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005103:Expert Systems; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D008403:Mass Screening; D013058:Mass Spectrometry / Q000379:methods; D008659:Metabolic Diseases / Q000175:diagnosis* / Q000378:metabolism*; D008954:Models, Biological; D015997:Neonatal Screening / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D015415:Biomarkers