Classification of congenital hypothyroidism in newborn screening using self-organizing maps - Gauran, Iris Ivy M.; Poblador, Maria Sofia Criselda A. (2012)
The Newborn Screening Reference Center (NSRC) of the National Institutes of Health 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." We investigate the efficiency of Self-Organizing Kohonen Maps (SOM) - a type of artificial neural network - as a tool for classification, particularly in detecting outliers.

A SOM with higher learning rate and larger training sample size yield low misclassification rate. A bootstrap estimate of the variability of the misclassification error rate is approximately 5%. 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: Newborn infant medical examinations; Medical screening; Congenital hypothyroidism -- Diagnosis; Self-organizing systems