A Multi-Voting Enhancement for Newborn Screening Healthcare Information System

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.

Newborn screening data management: proposing a framework for Iran

“…Since children are at risk of various diseases, such an investment may largely improve their health status and as a result, the community will move towards a healthier future 1 , 2. The newborn screening is one of the approaches that have prevented a number of diseases and congenital disorders in the past 40 years 3 , 4.…”