Evaluation of different machine learning methods for caesarean data classification

Автор: O.S.S. Alsharif, K.M. Elbayoudi, A.A.S. Aldrawi, K. Akyol

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 5 vol.11, 2019 года.

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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.

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

Короткий адрес: https://sciup.org/15016188

IDR: 15016188   |   DOI: 10.5815/ijieeb.2019.05.03

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