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The Fetal Medicine
Foundation
Computational Intelligent Diagnostic System
in predicting preeclampsia
Andreas Neocleous
Computational Intelligence
• Artificial neural networks
Kypros Nicolaides
Christos Schizas
Kleanthis Neokleous
Natasa Schiza
Costas Neocleous
• Evolutionary systems / Genetic algorithms
• Artificial immune systems
• Fuzzy systems
FMF, University of Cyprus, Cyprus University of Technology, Cyprus
The Fetal Medicine
Foundation
Computational Intelligent System
in predicting preeclampsia
Objective: Use computational intelligence to predict preeclampsia at 11-13 wks
All data:
Artificial Neural Network
Architecture
Total singleton pregnancies 13,538
No preeclampsia
13,118 (96.9%)
Preeclampsia
420 (3.1%)
Input (12 neurons)
Maternal characteristics
Obstetric & medical history
CRL, PAPP-A, Uterine PI, MAP
(Linear activation)
Data for training and validations:
Hidden Layer 1 (80 neurons)
(Logistic-sigmoid activation)
Unbalanced data
Training various ANNs: 335 PE, 10,496 unaffected by PE
Totally unknown cases used for validations:
85 PE, 2,622 unaffected by PE
Balanced data
Training various ANNs: 335 PE, 352 unaffected by PE
Totally unknown cases used for validations:
85 PE, 88 unaffected by PE
Hidden Layer 2 (10 neurons)
(Symmetric logistic activation)
Hidden Layer 3 (80 neurons)
(Logistic-sigmoid activation)
Output Layer (1 neurons)
Risk of preeclampsia
(Logistic-sigmoid activation)
The Fetal Medicine
Foundation
Computational Intelligent System
in predicting preeclampsia
Results on the unknown validation (verification) data set:
Unbalanced data
Classification
ALL cases
Predicted Correct
Unaffected
2,622
1,957 (74.6%)
Preeclampsia
85
38 (44.7%)
Balanced data
Classification
ALL cases
Predicted Correct
Unaffected
88
80 (90.9%)
Preeclampsia
85
80 (94.1%)
The Fetal Medicine
Foundation
Computational Intelligent System
in predicting preeclampsia
Conclusions
 Very encouraging findings regarding classification by means of
Computational Intelligence
 Future work should aim to reduce the normal data in a systematic
way so that the new reduced set will reflect the whole database of
cases.
 Some ‘unaffected’ cases were repeatedly wrongly categorized as ‘PE’
in almost all of the examined networks. When we compared the
inputs of these cases with the inputs of some true ‘PE’ cases, we
observed that they were quite similar.
Thank you