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

Nurul Chamidah, Ito Wasito

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-41
Number of pages5
ISBN (Electronic)9781509003624
DOIs
Publication statusPublished - 19 Feb 2016
EventInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015 - Depok, Indonesia
Duration: 10 Oct 201511 Oct 2015

Publication series

NameICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

ConferenceInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
Country/TerritoryIndonesia
CityDepok
Period10/10/1511/10/15

Keywords

  • cardiotocography (CTG)
  • clasification
  • feature extraction
  • fetal state
  • K-Means
  • SVM

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