期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：352-356
出版社：Science and Information Society (SAI)
摘要：Cardiotocography is a medical device that
monitors fetal heart rate and the uterine contraction during the
period of pregnancy. It is used to diagnose and classify a fetus
state by doctors who have challenges of uncertainty in data. The
Rough Neural Network is one of the most common data mining
techniques to classify medical data, as it is a good solution for the
uncertainty challenge. This paper provides a simulation of Rough
Neural Network in classifying cardiotocography dataset. The
paper measures the accuracy rate and consumed time during the
classification process. WEKA tool is used to analyse
cardiotocography data with different algorithms (neural
network, decision table, bagging, the nearest neighbour, decision
stump and least square support vector machine algorithm). The
comparison shows that the accuracy rates and time consumption
of the proposed model are feasible and efficient.
关键词：Accuracy rate; cardiotocography; data mining;
rough neural network; WEKA tool
