Year: 2012 Vol.: 61 No.: 1
Authors: Iris Ivy M. Gauran; Maria Sofia Criselda A. Poblador
Each day, the Newborn Screening Reference Center (NSRC) of the National Health Institute in University of the Philippines Manila collects measurements from five attributes to determine whether Congenital Hypothyroidism (CH) is present in a neonate. Detecting the CH cases is a major concern of medical practitioners because it provides richer information than the healthy ones. However, because of the rarity of this metabolic condition, existing classification algorithms oftentimes misclassify a newborn as “normal” even if it is not. This paper investigates the efficiency of Self-Organizing Kohonen Maps (SOM), a type of artificial neural network. Though it is widely known as a tool for visualization and clustering, the researchers want to probe on its ability as a tool for classification, particularly in detecting outliers. Results show that a lower misclassification rate yields from a self-organizing map with higher learning rate and larger training sample size. A bootstrap estimate of the variability of the misclassification error of roughly around 5% is also obtained. The misclassification error rate is lower when the original validation sample is used, compared to the average misclassification error rate computed from the bootstrap validation samples. Particularly, for a learning rate of 0.8 and a ratio of 2:1 training to validation sample, a 2.04% misclassification against 7.93% misclassification with 4.86% standard deviation is observed.
Keywords: self-organizing kohonen maps (SOM); classification algorithm; outlier detection; newborn screening for congenital hypothyroidism