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Cardiotocography is one
of the most widely used technique for recording changes in fetal heart rate
(FHR) and uterine contractions. Assessing cardiotocography is crucial in that
it leads to iden- tifying fetuses which suffer from lack of oxygen, i.e. hypoxia.
This situation is defined as fetal dis- tress and requires fetal intervention
in order to prevent fetus death or other neurological disease caused by
hypoxia. In this study a computer-based approach for analyzing cardiotocogram
in- cluding diagnostic features for discriminating a pathologic fetus. In order
to achieve this aim adaptive boosting ensemble of decision trees and various
other machine learning algorithms are employed.