Add to Marked List 2 of 2 A Multi Model Voting Enhancement for Newborn Screening Healthcare Information System

Sung-Huai Hsieh, Po Hsun Cheng, Sheau-Ling Hsieh, Po Hao Chen, Yung-Ching Weng, Yin-Hsiu Chien, Zhenyu Wang, Feipei Lai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.
Original languageEnglish
Title of host publication1st KES International Symposium on Intelligent Decision Technologies
PublisherSpringer
StatePublished - 2009

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