Classifying Cardiotocography Data based on Rough Neural Network
作者: Belal AminMona GamalA. A. SalamaI.M. El-HenawyKhaled Mahfouz
刊名: International Journal of Advanced Computer Science and Applications (IJACSA), 2019, Vol.10
来源数据库: The Science and Information Organization(SAI)
DOI: 10.14569/IJACSA.2019.0100846
关键词: Accuracy rateCardiotocographyData miningRough neural networkWEKA tool
原始语种摘要: 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...
全文获取路径: SAI 

  • fetus 胎儿
  • fetal 胎的
  • Data 数据
  • uncertainty 不定
  • mining 矿业
  • classify 分类
  • paper 
  • accuracy 准确度
  • decision 决定
  • consumption 消耗