telfor 2006

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neurons was determined by the WEKA system by maximising the .... Information Systems and Technologies (CISTI), 2011 6th Iberian. Conference on , vol., no., ...
TELFOR 2014 International IEEE Conference

Performance Comparison of Machine Learning Algorithms for Diagnosis of Cardiotocograms with Class Inequality Ioannis Chr. Stylios, Vasileios Vlachos, Member, IEEE, Iosif Androulidakis, Member, IEEE  Abstract — The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification – categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also placed on the performance of the Machine learning algorithms and the comparison of the classifiers’ results. Keywords — Artificial Neural Networks, Cardiotocograms, Machine Learning Algorithms, WEKA.

I. INTRODUCTION HE research attempt of this work focuses on the assessment of the condition of the health of the embryo, using contemporary techniques for the elaboration and analysis of the Fetal Heart Rate (FHR). More specifically, a correlation is made between the FHR and the blood pH level of the embryo, which is done by taking a sample from the umbilical cord immediately after birth. The low value of pH (7.1). • Abnormal / Suspicious. It refers to the embryos which suffered hypoxia to some extent (pH