Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization

In this work we explore the relevance of markers that are used for the early detection of fetal chromosomal abnormalities. For medical applications, it is important to optimize the number of used markers with respect to the number of necessary clinical examinations. We use the Generalized Matrix Learning Vector Quantization (GMLVQ) method to identify the most relevant markers from a set of 18 clinical examinations. We cross-validated our results using ten different training and test sets and we repeated our experiments using different parameters of GMLVQ. We identified the seven most relevant markers and we found that with these seven markers we obtain results that are comparable with the results that can be achieved with the full set of 18 markers. The results are in line with previous work that is found in the literature.