| Yazarlar |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye |
| Özet |
| Chronic kidney disease is a gradual loss of kidney function. Determining the important attributes that describe this disease plays a key role in screening and examining the disease by field specialists. The main aim of this study is to comprehensively compare the attribute selection algorithms for predicting this disease. With this aim, several models were built and compared using well-known performance metrics such as accuracy, sensitivity, and specificity in the experiments. Two different attribute selection methods; the stability selection and the minimum redundancy maximum relevance were compared comprehensively on the unbalanced and balanced datasets. In this framework, the stability selection method gave the important attributes. The support vector machines with radial bases function kernel successfully performed the classification using these attributes for this problem. |
| Anahtar Kelimeler |
| Chronic kidney disease | machine learning | significance of attribute selection | support vector machines |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Journal of Mechanics in Medicine and Biology |
| Dergi ISSN | 0219-5194 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2022 |
| Cilt No | 22 |
| Sayı | 10 |
| Sayfalar | 1 / 18 |
| Doi Numarası | 10.1142/S0219519422500658 |
| Makale Linki | http://dx.doi.org/10.1142/s0219519422500658 |
| Atıf Sayıları |
