Kardi̇otokogram Veri̇si̇nden Fetal İyi̇li̇k Hali̇ni̇n Beli̇rlenmesi̇ İçi̇n Bi̇r Karar Destek Si̇stemi̇

Abstract

A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram DataAbstract: 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.

Gömülü Si̇stem Tabanli Bi̇r Hatali Ürün Tespi̇t Si̇stemi̇

“…Bu çalışmada ele alınan problem hatalı ürün (tolerans dışı) ve normal ürün (tolerans içi) olmak üzere iki sınıflıdır. İki sınıflı bir sınıflandırma problemine ait hata matrisi, doğruluk, duyarlılık ve özgüllük tanımları Tablo 1 ve (6)-(8) denkleri ile verilmiştir (Kohavi ve Provost, 1998;Yılmaz, 2016).…”