Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine

N. Chamidah, Ito Wasito
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引用次数: 25

Abstract

Cardiotocography (CTG) records fetal heart rate (FHR) signal and intra uterine pressure (IUP) simultaneously. CTG are widely used for diagnosing and evaluates pregnancy and fetus condition until before delivery. The high dimension of CTG data are the problem for classification computation, by extracting feature we can get the useful information from CTG data, and in this research, K-Means Algorithm are used. After extracting useful information, data are trained by using Support Vector Machine (SVM) to obtain classifier for classifying the new incoming CTG data. Based on 10 cross validation, this method have a good accuracy to 90.64% using Cardiotocography Dataset obtained from UCI Machine Learning Repository. Data are classified into fetal state normal, suspicious, or pathologic class based on seven abstract features that extracted from twenty one original features and then trained using hybrid K-SVM Algorithm. This research shows the ability and capability of Hybrid K-SVM for classifying CTG dataset. In general, the experimental result of hybrid K-SVM show the better classification compare to SVM.
基于混合k均值和支持向量机特征提取的心电胎儿状态分类
心脏造影(CTG)同时记录胎儿心率(FHR)信号和子宫内压(IUP)。CTG广泛用于产前诊断和评估妊娠和胎儿状况。CTG数据的高维是分类计算的难题,通过提取特征可以从CTG数据中得到有用的信息,本研究采用K-Means算法。提取有用信息后,使用支持向量机(SVM)对数据进行训练,得到分类器,用于对新输入的CTG数据进行分类。基于10次交叉验证,该方法使用UCI机器学习存储库中获得的cardiotography Dataset,准确率达到90.64%。从21个原始特征中提取7个抽象特征,然后使用混合K-SVM算法对数据进行训练,将数据分为胎儿状态正常、可疑或病理三类。研究表明了混合K-SVM对CTG数据集进行分类的能力和能力。总的来说,混合K-SVM的实验结果比支持向量机分类效果更好。
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