A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data

Autor:	Ersen YILMAZ
Jazyk:	English<br />Turkish
Rok vydání:	2016
Předmět:	Cardiotocogram Decision support system Support vector machines Principal component analysis fetal well-being Technology Engineering (General). Civil engineering (General) TA1-2040
Zdroj:	Uludağ University Journal of The Faculty of Engineering, Vol 21, Iss 2, Pp 331-340 (2016)
Druh dokumentu:	article
ISSN:	2148-4147 2148-4155
DOI:	10.17482/uujfe.12556
Popis:	In this study, we propose a decision support system for assessment of fetal well-being from cardiotocogram data. The system is based on Principal Component Analysis and Least Squares Support Vector Machines. Principal Component Analysis is used for feature reduction of the cardiotocogram data set. Classification of the data set with reduced features is made by using Least Squares Support Vector Machines. Performance analysis of the proposed system is examined on the cardiotocogram data set availabe on UCI Machine Learning Repository by using 10-fold Cross Validation procedure. Experimetal results show that the proposed system has %98,74 classification accuracy, %98,86 sensitivity and %98,73 specificity rates.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/b0701046d5744bc0ab745131dccfd28d Zobrazit plný text záznamu View record in DOAJ