Ethnicity as a factor for the estimation of the risk for preeclampsia: A neural network approach
Date Issued
2010
DOI
10.1007/978-3-642-12842-4_49
Abstract
A large number of feedforward neural structures, both standard
multilayer and multi-slab schemes have been applied to a large data base of
pregnant women, aiming at generating a predictor for the risk of preeclampsia
occurrence at an early stage. In this study we have investigated the importance
of ethnicity on the classification yield. The database was composed of 6838
cases of pregnant women in UK, provided by the Harris Birthright Research
Centre for Fetal Medicine in London. For each subject 15 parameters were
considered as the most influential at characterizing the risk of preeclampsia
occurrence, including information on ethnicity. The same data were applied to
the same neural architecture, after excluding the information on ethnicity, in
order to study its importance on the correct classification yield. It has been
found that the inclusion of information on ethnicity, deteriorates the prediction
yield in the training and test (guidance) data sets but not in the totally
unknown verification data set.
multilayer and multi-slab schemes have been applied to a large data base of
pregnant women, aiming at generating a predictor for the risk of preeclampsia
occurrence at an early stage. In this study we have investigated the importance
of ethnicity on the classification yield. The database was composed of 6838
cases of pregnant women in UK, provided by the Harris Birthright Research
Centre for Fetal Medicine in London. For each subject 15 parameters were
considered as the most influential at characterizing the risk of preeclampsia
occurrence, including information on ethnicity. The same data were applied to
the same neural architecture, after excluding the information on ethnicity, in
order to study its importance on the correct classification yield. It has been
found that the inclusion of information on ethnicity, deteriorates the prediction
yield in the training and test (guidance) data sets but not in the totally
unknown verification data set.