Incapable of identifying suspicious records in CTG data using ANN based machine learning techniques

摘要

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. We implement a model based CTG data classification system using a supervised artificial neural network (ANN) and support vector machine (SVM) which can classify the CTG data based on its training data. The performance neural network based classification model has been compared with the most commonly used unsupervised clustering methods Fuzzy C-mean and k-mean clustering and supervised clustering method SVM classification. According to the arrived results, the performance of the supervised machine learning based classification (ANN) approach provided significant performance than other compared unsupervised clustering methods and supervised SVM classification method. We used Precision, Recall, F-Measure and Rand Index as the metric to evaluate the performance. Even though the traditional clustering methods can identify the Normal CTG patterns, they were incapable of finding Suspicious and Pathologic patterns and the SVM based classifier provided good performance, it was absolutely incapable of identifying a single suspicious record. It was found that, the ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CTG data with very good accuracy. The important finding in this paper is Even though SVM is a well proven technique for classification, it was incapable of identifying Suspicious Records in Cardiotocogram Data - but ANN did considerably good classification of Suspicious Records.