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Math. Comput. Appl. 2010, 15(3), 472-480; doi:10.3390/mca15030472

Intrauterine Growth Restriction (IUGR) Risk Decision Based on Support Vector Machines

1
Boğaziçi University, Dept. of Computer Eng. Bebek, 34342 Istanbul, Turkey
2
Trakya University, Ob/Gyn Dept, Edirne, Turkey
*
Authors to whom correspondence should be addressed.
Published: 1 December 2010
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Abstract

This paper studies the risk of intrauterine growth restriction (IUGR) using support vector machines (SVM). A structured and globally optimized SVM system may be preferable procedure in the identification of IUGR fetus at risk. The IUGR risk is estimated in two stages: in the first stage, noninvasive Doppler pulsatility index (PI) and resistance index (RI) of umbilical artery (UA), middle cerebral artery (MCA) and ductus venosus (DV) and amniotic fluid index (AFI) are retrospectively analyzed and the Doppler indices are applied to the SVM system to make a diagnosis decision on the fetal wellbeing as ”reactive” or “nonreactive and/or acute fetal distress (AFD)” on the nonstress test (NST) (training data). In the second stage (testing data), the decision is validated by the NST (target value). Experiments are performed on previously collected data. Fortyfour preterm with IUGR and without IUGR pregnancies before 34 weeks gestation are considered.The nonparametric Bayes-risk decision rule, k-nearest neighbor (k-NN), is used for comparison. It is observed that the SVM system is proven to be useful in predicting the expected risk in IUGR cases in this small population study. The PI and RI values of UA, MCA and DV are also effective in distinguishing IUGR at risk.
Keywords: Intrauterine growth restriction (IUGR); Doppler indices PI and RI; support vector machines (SVM); k-NN Intrauterine growth restriction (IUGR); Doppler indices PI and RI; support vector machines (SVM); k-NN
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zengin, Z.; Gürgen, F.; Varol, F. Intrauterine Growth Restriction (IUGR) Risk Decision Based on Support Vector Machines. Math. Comput. Appl. 2010, 15, 472-480.

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