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.
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 Sundar, C., Chitradevi, M. and Geetharamani, G. (2012) Classification of Cardiotocogram Data Using Neural Network Based Machine Learning Technique. International Journal of Computer Applications, 47, 19-25. http://dx.doi.org/10.5120/7256-0279
 Sundar, C., Chitradevi, M. and Geetharamani, G. (2013) An Overview of Research Challenges for Classification of Cardiotocogram Data. Journal of Computer Science, 9, 198-206. http://dx.doi.org/10.3844/jcssp.2013.198.206
 Y?lmaz, E. and K?l?k??er, ?. (2013) Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree. Computational and Mathematical Methods in Medicine, 2013, 2013.