Abstract
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.
Highlights
Cardiotocography is a worldwide technique for fetal monitoring
Huang and Hsu [2] proposed discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) in their study to evaluate fetal distress by the same CTG data used in this study
They reached the results showing that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78% respectively, and 80%, 10%, and the remaining 10% of the whole dataset were randomly used for training, testing, and validation respectively
Summary
Cardiotocography ( called as electronic fetal monitoring, EFM) is a worldwide technique for fetal monitoring. Huang and Hsu [2] proposed discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) in their study to evaluate fetal distress by the same CTG data used in this study. They reached the results showing that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78% respectively, and 80%, 10%, and the remaining 10% of the whole dataset were randomly used for training, testing, and validation respectively. (2014) Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach. Contribution of AdaBoost to classification algorithms is analyzed with respect to CTG data
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