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Effective Diagnostic System of Fetal Heart Disease Based on Cardiotocography using AdaBoost


D. Rubiya Sweetlin , VV College of Engineering; Dr. I. Muthulakshmi, VV College of Engineering


AdaBoost, Cardiotocography, FHR, Machine Learning, Naïve Bayes, SVM


Fetal health is commonly surveilled by cardiotocography (CTG) monitor, which documents fetal heart rate (FHR) signal. The fetal health monitoring is very important to predict the growth of the fetal as well as improvement in its each stage. For yielding the status of the fetal more accurately, fetal heart rate (FHR) signal is the best well known source. From this gained information, embryo’s unhealthiness can be visualized and it provides a chance to take action before an irremediable harmness happening to the fetal. Previous work uses Naïve Bayes and SVM classifier, which was used in the olden days, to classify the CTG data, produced low accuracy. To improve the classification accuracy, the proposed work addresses Adaptive Boosting algorithm for classification of congenital heart disease in fetus. Fetal state is commonly grouped into normal and pathological class. The results of these classifiers are to be compared and the best classification technique is to be identified.

Other Details

Paper ID: IJSRDV7I10467
Published in: Volume : 7, Issue : 1
Publication Date: 01/04/2019
Page(s): 606-609

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