The metabolic disorders may hinder an infant's normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially; it also handles the medical resources effectively and efficiently.
- Newborn screening; Tandem mass spectrometry; Support Vector Machine
- CUTOFF VALUES; STRATEGIES; AGE
Chen, W-H., Chen, H-P., Tseng, Y-J., Hsu, K-P., Hsieh, S-L., Chien, Y-H., Hwu, W-L., & Lai, F. (2012). Newborn Screening for Phenylketonuria: Machine Learning vs Clinicians. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 798-803). IEEE. https://doi.org/10.1109/ASONAM.2012.145