Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model

• Autorzy: Shafaei Bajestani N. , Vahidian Kamyad A. , Nasli Esfahani E. , Zare A.

Identyfikatory

DOI

10.1016/j.bbe.2017.01.003

• Języki publikacji: EN

Abstrakty

EN

Choosing a proper method to predict and timely prevent the complications of diabetes could be considered a significant step toward optimally controlling the disease. Since in medical research only small sample sizes of data are available and medical data always includes high levels of uncertainty and ambiguity, a type-2 fuzzy regression model seems to be an appropriate procedure for finding the relationship between outcome and explanatory variables in medical decision-making. In this paper, a new type-2 fuzzy regression model based on type-2 fuzzy time series concepts is used to forecast nephropathy in diabetic patients. Results in two examples show model efficiency. The use of such models in diabetes clinics is proposed.

Słowa kluczowe

EN

type-2 fuzzy logic; fuzzy time series; fuzzy regression; nephropathy forecasting

PL

zbiór rozmyty typu 2; szereg czasowy rozmyty; regresja rozmyta; nefropatia

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 2
• Strony: 281--289
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Shafaei Bajestani N.

• Department of Electrical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran

autor

Vahidian Kamyad A.

• Department of Electrical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran

autor

Nasli Esfahani E.

• Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

autor

Zare A.

• Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).