Neural networks to estimate the risk for preeclampsia occurrence
Date Issued
2009
DOI
10.1109/IJCNN.2009.5178820
Abstract
A number of neural network 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. 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,
24 parameters were measured or recorded. Out of these,
15 parameters were considered as the most influencing at characterizing
the risk of preeclampsia occurrence. A number of
feedforward neural structures, both standard multilayer and
multi-slab, were tried for the prediction. The best results obtained
were with a multi-slab neural structure. In the training
set there was a correct classification of the 83.6% cases of preeclampsia
and in the test set 93.8%. The preeclampsia cases
prediction for the totally unknown verification test was 100%.
applied to a large data base of pregnant women, aiming at generating
a predictor for the risk of preeclampsia occurrence at
an early stage. 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,
24 parameters were measured or recorded. Out of these,
15 parameters were considered as the most influencing at characterizing
the risk of preeclampsia occurrence. A number of
feedforward neural structures, both standard multilayer and
multi-slab, were tried for the prediction. The best results obtained
were with a multi-slab neural structure. In the training
set there was a correct classification of the 83.6% cases of preeclampsia
and in the test set 93.8%. The preeclampsia cases
prediction for the totally unknown verification test was 100%.
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