TITLE

Evaluation of Different Machine Learning Methods for Caesarean Data Classification

AUTHOR(S)
Alsharif, O. S. S.; Elbayoudi, K. M.; Aldrawi, A. A. S.; Akyol, K.
PUB. DATE
September 2019
SOURCE
International Journal of Information Engineering & Electronic Bu;Sep2019, Vol. 11 Issue 5, p19
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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.
ACCESSION #
138746998

 

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