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

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

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 2

281--289

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.

Using support vector regression in gene selection and fuzzy rule generation for relapse time prediction of breast cancer

Mahmoodian H., Ebrahimian L.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 3

466--472

Gene expression profiles have been recently used in survival analysis, tumor classification and ER status identification. The prediction of breast cancer recurrence based on gene expression profile has been regarded in some previous studies in which the procedures were based on the concept of regression functions and fuzzy systems. In this study, a method based on the combination of these two concepts is presented; not only a method for gene selection, but also a systematic way to create fuzzy rules are going to be offered. Due to the ability of type-2 fuzzy systems in handling of uncertain systems, the proposed model is developed to type-2. The results show that this model has been improved in comparison to previous ones.