Neural networks to investigate the effects of smoking and alcohol abuse on the risk for preeclampsia

Following the application of a large number of neural network schemes that 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, we investigated the importance of the parameters of smoking and alcohol intake on the classification yield. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. 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 influential at characterizing the risk of preeclampsia occurrence, including the characteristics on whether the pregnant woman was an active smoker or not, and on whether she was consuming alcohol. The same data were applied to the same neural architecture, after excluding the information on smoking and alcohol, in order to study the importance of these two parameters on the correct classification yield. It has been found that both information parameters, were needed in order to achieve a correct classification as high as 83.6% of preeclampsia cases in the whole dataset, and of 93.8% in the test set. The preeclampsia cases prediction, for the totally unknown verification test, was 100%. When information on smoking and alcohol intake were not used, the results deteriorated significantl

Neural networks to investigate the effects of smoking and alcohol abuse on the risk for preeclampsia

Following the application of a large number of neural network schemes that 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, we investigated the importance of the parameters of smoking and alcohol intake on the classification yield. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. 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 influential at characterizing the risk of preeclampsia occurrence, including the characteristics on whether the pregnant woman was an active smoker or not, and on whether she was consuming alcohol. The same data were applied to the same neural architecture, after excluding the information on smoking and alcohol, in order to study the importance of these two parameters on the correct classification yield. It has been found that both information parameters, were needed in order to achieve a correct classification as high as 83.6% of preeclampsia cases in the whole dataset, and of 93.8% in the test set. The preeclampsia cases prediction, for the totally unknown verification test, was 100%. When information on smoking and alcohol intake were not used, the results deteriorated significantl

Ethnicity as a factor for the estimation of the risk for preeclampsia: A neural network approach

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

First trimester diagnosis of trisomy-21 using artificial neural networks

Langdon Down in 1866 reported on a syndrome in which individuals have skin appearing to be too large for the body, a nose small and a flat face. This is a chromosomal disorder caused by the presence of all or part of an extra 21st chromosome, and is known as the Down syndrome, or trisomy 21, or trisomy G. In the last fifteen years it has become possible to observe these features by ultrasound examination in the third month of intrauterine life. About 75% of trisomy 21 fetuses have absent nasal bone. In the present work, neural network schemes that have been applied to a large data base of findings from ultrasounds of fetuses, aiming at generating a predictor for the risk of Down syndrome are reported. A good number of feed forward neural structures, both standard multilayer and multi-slab, were tried for the prediction. The database was composed of 23513 cases of fetuses in UK, provided by the Fetal Medicine Foundation in London. For each subject, 19 parameters were measured or recorded. Out of these, 19 parameters were considered as the most influential at characterizing the risk for this type of chromosomal defect. The best results obtained were with a multi-slab neural structure. In the training set there was a correct classification of the 98.9% cases of trisomy 21 and in the test set 100%. The prediction for the totally unknown verification test set was 93.3%

First trimester diagnosis of trisomy-21 using artificial neural networks

Langdon Down in 1866 reported on a syndrome in which individuals have skin appearing to be too large for the body, a nose small and a flat face. This is a chromosomal disorder caused by the presence of all or part of an extra 21st chromosome, and is known as the Down syndrome, or trisomy 21, or trisomy G. In the last fifteen years it has become possible to observe these features by ultrasound examination in the third month of intrauterine life. About 75% of trisomy 21 fetuses have absent nasal bone. In the present work, neural network schemes that have been applied to a large data base of findings from ultrasounds of fetuses, aiming at generating a predictor for the risk of Down syndrome are reported. A good number of feed forward neural structures, both standard multilayer and multi-slab, were tried for the prediction. The database was composed of 23513 cases of fetuses in UK, provided by the Fetal Medicine Foundation in London. For each subject, 19 parameters were measured or recorded. Out of these, 19 parameters were considered as the most influential at characterizing the risk for this type of chromosomal defect. The best results obtained were with a multi-slab neural structure. In the training set there was a correct classification of the 98.9% cases of trisomy 21 and in the test set 100%. The prediction for the totally unknown verification test set was 93.3%

Artificial neural networks for non-invasive chromosomal abnormality screening of fetuses

A large number of different neural network structures have been constructed, trained and tested to a large data base of pregnant women characteristics, aiming at generating a classifier-predictor for the presence of chromosomal abnormalities in fetuses, namely the Trisomy 21 (Down syndrome), Trisomy 18 (Edwards syndrome), Trisomy 13 (Patau syndrome) and the Turner syndrome. The database was composed of 31611 cases of pregnant women. 31135 women did not show any chromosomal abnormalities, while the remaining 476 were confirmed as having a chromosomal anomaly of T21, T18, T13, or Turner Syndrome. From the total of 31611 cases, 8191 were kept as a totally unknown database that was only used for the verification of the predictability of the network. In this set, 7 were of the Turner syndrome, 14 of the Patau syndrome, 42 of the Edwards syndrome and 71 of the Down syndrome. For each subject, 10 parameters were considered to be the most influential at characterizing the risk of occurrence of these types of chromosomal anomalies. The best results were obtained when using a multi-layer neural structure having an input, an output and three hidden layers. For the case of the totally unknown verification set of the 8191 cases, 98.1% were correctly identified. The percentage of abnormal cases correctly predicted was 85.1%. The unknown T21 cases were predicted by 78.9%, the T18 by 76.2%, the T13 by 0.0% and the Turner syndrome by 42.9%

Ethnicity as a factor for the estimation of the risk for preeclampsia: A neural network approach

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

Neural networks to estimate the risk for preeclampsia occurrence

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%

Neural networks to estimate the risk for preeclampsia occurrence

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%

Artificial neural networks to investigate the significance of PAPPA and b-hCG for the prediction of chromosomal abnormalities

A systematic approach has been done, to investigate different neural network structures for the appraisal of the significance of the free b-human chorionic gonadotrophin (b-hCG) and the pregnancy associated plasma protein-A (PAP-PA) as important parameters for the prediction of the existence of chromosomal abnormalities in fetuses. The database that has been used was highly unbalanced. It was composed of 35,687 cases of pregnant women. In the vast majority of cases (35,058) there had not been any chromosomal abnormalities, while in the remaining 629 (1.76%) some kind of chromosomal defect had been confirmed. 8,181 cases were kept as a totally unknown database that was used only for the verification of the predictability of each network, and for evaluating the importance of PAPP-A and b-hCG as significant predicting factors. In this unknown data set, there were 76 cases of chromosomal defects. The system was trained by using 8 input parameters that were considered to be the most influential at characterizing the risk of occurrence of these types of chromosomal anomalies. Then, the PAPP-A and the b-hCG were removed from the inputs in order to ascertain their contributory effects. The best results were obtained when using a multilayer neural structure having an input, an output and two hidden layers. It was found that both of PAPP-A and b-hCG are needed in order to achieve high correct classifications and high sensitivity of 88.2% in the totally unknown verification data set. When both the b-hCG and PAPP-A were excluded from the training, the diagnostic yield dropped down to 65%