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