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Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model

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  • Shafaei Bajestani, Narges
  • Vahidian Kamyad, Ali
  • Nasli Esfahani, Ensieh
  • Zare, Assef

Abstract

Due to the small sample size of data available in medical research and the levels of uncertainty and ambiguity associated with medical data, some researchers have employed fuzzy regression models to find the relationship between outcomes and explanatory variables in medical decision-making. The advantages of regression models are their ability to handle small sample sizes while fuzzy logic can model vagueness, thus making fuzzy regression a popular model among researchers. In addition, the high levels of uncertainty in medical data encourage the use of type-2 fuzzy which is capable of handling such uncertainty. The current paper proposes an interval type-2 fuzzy regression model for predicting retinopathy in diabetic patients. The results of the present work shall prevent unnecessary testing of diabetic patient. This study also aims to assist patients and the healthcare community to reduce the cost of diabetes control and treatment by optimizing the number of check-ups.

Suggested Citation

  • Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
    • Handle: RePEc:eee:ejores:v:264:y:2018:i:3:p:859-869
    DOI: 10.1016/j.ejor.2017.07.046
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    References listed on IDEAS

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    1. Mason, J.E. & Denton, B.T. & Shah, N.D. & Smith, S.A., 2014. "Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 727-738.
    2. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
    3. Kao, Chiang & Chyu, Chin-Lu, 2003. "Least-squares estimates in fuzzy regression analysis," European Journal of Operational Research, Elsevier, vol. 148(2), pages 426-435, July.
    4. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
    5. Ali Azadeh & S. Mohammad Hasan Manzour Alajdad & Tahereh Aliheidari Bioki, 2014. "A neuro-fuzzy regression approach for estimation and optimisation of gasoline consumption," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 17(2), pages 221-256.
    6. Qin, Jindong & Liu, Xinwang & Pedrycz, Witold, 2017. "An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment," European Journal of Operational Research, Elsevier, vol. 258(2), pages 626-638.
    7. Chen, Ting-Yu & Chang, Chien-Hung & Rachel Lu, Jui-fen, 2013. "The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making," European Journal of Operational Research, Elsevier, vol. 226(3), pages 615-625.
    8. H. Hassanpour & H. R. Maleki & M. A. Yaghoobi, 2009. "A Goal Programming Approach To Fuzzy Linear Regression With Non-Fuzzy Input And Fuzzy Output Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 26(05), pages 587-604.
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