An Analysis on the Performance of Fuzzy C -Means Clustering Algorithm for Cardiotocogram Data Clustering

Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of Cardiotocography into clinical practice, the predictive capacity of the methods remains controversial and still inaccurate. In this paper, we evaluate commonly used unsupervised clustering method Fuzzy C-mean clustering for their suitability towards clustering CTG data. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. In previous work, the overall Precision, Recall and F-Score were only considered. But in this evaluation, we are going to measure class-wise Precision, Recall and F-Score to make the analysis very specific. The arrived results prove that, even though the traditional clustering methods can identify the Normal CTG patterns, they were incapable of Suspicious and Pathologic patterns. This fact was not highlighted in the previous work.