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Evaluation of Different Machine Learning Methods for Caesarean Data Classification    
Yazarlar (4)
Oss Alsharif
Km Elbayoudi
Aas Aldrawi
Doç. Dr. Kemal AKYOL Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye
Devamını Göster
Özet
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.
Anahtar Kelimeler
Makale Türü Özgün Makale
Makale Alt Türü Diğer hakemli uluslarası dergilerde yayınlanan tam makale
Dergi Adı International Journal of Information Engineering and Electronic Business
Dergi ISSN 2074-9023 Scopus Dergi
Dergi Tarandığı Indeksler CMCI: CompuMath Citation Index
Makale Dili İngilizce
Basım Tarihi 09-2019
Cilt No 11
Sayı 5
Sayfalar 19 / 23
Doi Numarası 10.5815/ijieeb.2019.05.03
Makale Linki http://www.mecs-press.org/ijieeb/ijieeb-v11-n5/v11n5-3.html
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Google Scholar 9
Evaluation of Different Machine Learning Methods for Caesarean Data Classification

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