Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Polish Acad Sciences Inst Physics
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.
Açıklama
3rd International Conference on Computational and Experimental Science and Engineering (ICCESEN) -- OCT 19-24, 2016 -- Antalya, TURKEY
Anahtar Kelimeler
cardiotocography, machine learning techniques, classification
Kaynak
Acta Physica Polonica A
WoS Q Değeri
Q3
Scopus Q Değeri
Q4
Cilt
132
Sayı
3
