Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data
Van den Bulcke, Tim × Vanden Broucke, Paul Van Hoof, Viviane Wouters, Kristien vanden Broucke, Seppe Smits, Geert Smits, Elke Proesmans, Sam Van Genechten, Toon Eyskens, Francois #
Journal of Biomedical Informatics vol:44 issue:2 pages:319-325
Newborn screening programs for severe metabolic disorders using tandem mass spectrometry are widely used. Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid oxidation defect (1:15,000 newborns) and it has been proven that early detection of this metabolic disease decreases mortality and improves the outcome. In previous studies, data mining methods on derivatized tandem MS datasets have shown high classification accuracies. However, no machine learning methods currently have been applied to datasets based on non-derivatized screening methods.