Evaluation of Different Machine Learning Methods for Caesarean Data Classification
Yazarlar (4)
| Makale Türü |
Özgün Makale
(Diğer hakemli uluslarası dergilerde yayınlanan tam makale)
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| 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 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 08-09-2019 |
| Cilt / Sayı / Sayfa | 11 / 5 / 19–23 | DOI | 10.5815/ijieeb.2019.05.03 |
| Makale Linki | http://www.mecs-press.org/ijieeb/ijieeb-v11-n5/v11n5-3.html | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| 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 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 10 |