ICCESEN 2016 (2)

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

In the ICCESEN, I presented two works which are entitled as "Performance Comparison of Artificial Neural Network Training Algorithm for Fetal Heart Rate Patterns" and "Comparison of Machine Learning Techniques for Fetal Heart Rate Classification".

Prof. Gerhard-Wilhelm WEBER from Middle East Technical University was an invited speaker and attended my sessions. He is very fun and a great scientist. I would like to thanks him and Dr. Çiğdem GÖKÇEK-SARAÇ for their kindly and encouraging comments.

The full text of paper will be published in the first period of 2017.

"Comparison of Machine Learning Techniques for Fetal Heart Rate Classification"

Abstract

Cardiotocography is a monitoring technique that provides 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 patterns. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. Therefore, research has been focused on different machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative study, fetal heart rate patterns are classified as healthy or unhealthy. The aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate patterns. The study results show that the artificial neural network has produced the most efficient classification success.

Keywords: Cardiotocography, machine learning techniques, classification, artificial neural network, support vector machine, extreme machine learning, radial basis function network, random forest

2016-10-30, Pazar
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