Kronik Böbrek Hastalığının Erken Tanısı için Yeni Bir Klinik Karar Destek Sistemi
Öz
Anahtar Kelimeler
Kaynakça
- Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37-66.
- Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2013, December). Clinical decision support system for diagnosis and management of chronic renal failure. In 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1-6). IEEE.
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
- Cleary, J. G., & Trigg, L. E. (1995, July). K*: An instance-based learner using an entropic distance measure. In 12th International Conference on Machine Learning (pp. 108-114).
- Cottrell, G. W., & Munro, P. (1988, October). Principal components analysis of images via back propagation. In Visual Communications and Image Processing'88: Third in a Series (pp. 1070-1077). International Society for Optics and Photonics.
- Couser, W. G., Remuzzi, G., Mendis, S., & Tonelli, M. (2011). The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney International, 80(12), 1258-1270.
- Eyupoglu, C., Aydin, M. A., Zaim, A. H., & Sertbas, A. (2018). An efficient big data anonymization algorithm based on chaos and perturbation techniques. Entropy, 20(5), 373.
- Frank, E. (2014). Fully supervised training of Gaussian radial basis function networks in WEKA. Department of Computer Science, University of Waikato, Hamilton, New Zealand.
Ayrıntılar
Birincil Dil
Türkçe
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Can Eyüpoğlu
*
0000-0002-6133-8617
Türkiye
Yayımlanma Tarihi
31 Aralık 2020
Gönderilme Tarihi
27 Mayıs 2020
Kabul Tarihi
28 Ekim 2020
Yayımlandığı Sayı
Yıl 2020 Sayı: 20
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