Handling The Effect of Attribute Selection on Support Vector Machines For Detecting Chronic Kidney Disease
Yazarlar (2)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Journal of Mechanics in Medicine and Biology (Q2) | ||
| Dergi ISSN | 0219-5194 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2022 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 13-10-2022 |
| Cilt / Sayı / Sayfa | 22 / 10 / 1–18 | DOI | 10.1142/S0219519422500658 |
| Makale Linki | http://dx.doi.org/10.1142/s0219519422500658 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| 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 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |