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

Abstract

 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

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Keywords

• Cardiotocogram,Decision support system,Support vector machines,Principal component analysis,fetal well-being

KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ

Abstract

Bu çalışmada kardiotogram verisinden fetal iyilik halinin belirlenmesi için bir karar destek sistemi önerilmiştir.  Sistem En Küçük Kareler Destek Vektör Makineleri ve Temel Bileşen Analizi üzerinde temellendirilmiştir. Temel Bileşen Analizi yöntemi ile kardiotokogram veri kümesinin boyutu indirgenmiştir. Özellik boyutu indirgenen veri kümesi üzerinde En Küçük Kareler Destek Vektör Makineleri kullanılarak sınıflandırma işlemi gerçekleştirilmiştir. Önerilen karar destek sisteminin başarımı UCI Makine Öğrenmesi Ambarlarından alınan kardiotokogram veri kümesi üzerinde 10-katlı Çapraz Doğrulama tekniği kullanılarak incelenmiştir. Deneysel sonuçlar önerilen sistemin %98,74 sınıflandırma doğruluğuna, %98,86 duyarlılık oranına ve %98,73 özgüllük oranına sahip olduğunu göstermiştir.

 

Keywords

• Kardiotokogram,Karar destek sistemi,Destek vektör makineleri,Temel bileşen analizi,Fetal iyilik hali

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