Evaluation of Different Machine Learning Methods for Caesarean Data Classification

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O.S.S. Alsharif 1,* K.M. Elbayoudi 1 A.A.S. Aldrawi 1 K. Akyol 2

1. Department of Material Science and Engineering, Kastamonu University, Kastamonu, Turkey

2. Department of Computer Engineering, Kastamonu University, Kastamonu, Turkey

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2019.05.03

Received: 17 Jul. 2019 / Revised: 28 Jul. 2019 / Accepted: 5 Aug. 2019 / Published: 8 Sep. 2019

Index Terms

Caesarean data, machine learning, Decision Tree, K-Nearest- Neighbours, Naïve Bayes, Support Vector Machine, Random Forest Classifier.


Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved from California University website. The main objective of this study is to compare selected algorithms’ performances. This study has shown that the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.

Cite This Paper

O.S.S. Alsharif, K.M. Elbayoudi, A.A.S. Aldrawi, K. Akyol, "Evaluation of Different Machine Learning Methods for Caesarean Data Classification", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.5, pp. 19-23, 2019. DOI:10.5815/ijieeb.2019.05.03


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