An Analysis of Support Vector Machine And C4.5 for Classification of Cardiotocogram

The aim of this study is evaluating the classification performances of two machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby’s heart rate took from the mother’s abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. In this paper, we implement a model based CTG data classification system using a supervised SVM and C 4.5 algorithm which can classify the CTG data based on its training data. According to the arrived results, the performance of the C 4.5 based classification approach provided significant performance. We used Accuracy, Specificity and sensitivity, NPV, PPV and ROC as the metric to evaluate the performance. It was found that, the C 4.5 based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CTG data with very good accuracy.