Volume 18, Issue 2 (2-2019)
ijdld 2019, 18(2): 90-96 |
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Shafaei Bajestani N, Aradmehr M, Nasli Esfahani E, Khiabani tanha B. NEPHROPATHY PREDICTION IN DIABETIC PATIENT USING FUZZY REGRESSION MODEL. ijdld 2019; 18 (2) :90-96
URL: http://ijdld.tums.ac.ir/article-1-5751-en.html
URL: http://ijdld.tums.ac.ir/article-1-5751-en.html
1- Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran , narges.shafaei@gmail.com
2- Department of Midwifery, School of Nursing and Midwifery, Sabzevar University of Medical Sciences, Sabzevar, Iran
3- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
4- Parsian Diabetes Research Center, Mashhad, Iran
2- Department of Midwifery, School of Nursing and Midwifery, Sabzevar University of Medical Sciences, Sabzevar, Iran
3- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
4- Parsian Diabetes Research Center, Mashhad, Iran
Abstract: (3288 Views)
Background: Diabetes is one of the most dangerous and common diseases of the modern world. Since medical research usually has limited data available and medical data is very ambiguous, it seems appropriate to use the fuzzy model to find out the relationship between input and output in medical data. None of the previous articles of fuzzy regression have been used to predict complications of diabetes, including nephropathy. Therefore, in this study, a fuzzy regression model was used to predict nephropathy in a diabetic patient.
Methods: In the present study, GFR results of previous patient experiments were used to predict a deeper horizons of GFR and ultimately to predict renal disease. Chronic kidney disease has been stratified based on the amount of GFR, that fuzzy data has been constructed based on these levels. The GFR prediction was performed in the following steps. Step 1: Define fuzzy sets based on the GFR level, which is considered for each level of a fuzzy set. Step 2: Fuzzify patient data Based on fuzzy sets. Step 3: GFR prediction with fuzzy regression model. Step 4: Defuzzifying the predictions. Step 5: Evaluating the model efficiency. The RMSE error is used to compare the performance of the model.
Results: The results of GFR prediction showed that comparison RMSE was 10.09 with using simple linear regression model and 4.24 in fuzzy model.
Conclusions: fuzzy regression model can predict nephropathy in diabetic patients.
Methods: In the present study, GFR results of previous patient experiments were used to predict a deeper horizons of GFR and ultimately to predict renal disease. Chronic kidney disease has been stratified based on the amount of GFR, that fuzzy data has been constructed based on these levels. The GFR prediction was performed in the following steps. Step 1: Define fuzzy sets based on the GFR level, which is considered for each level of a fuzzy set. Step 2: Fuzzify patient data Based on fuzzy sets. Step 3: GFR prediction with fuzzy regression model. Step 4: Defuzzifying the predictions. Step 5: Evaluating the model efficiency. The RMSE error is used to compare the performance of the model.
Results: The results of GFR prediction showed that comparison RMSE was 10.09 with using simple linear regression model and 4.24 in fuzzy model.
Conclusions: fuzzy regression model can predict nephropathy in diabetic patients.
Type of Study: Applicable |
Subject:
Special
Received: 2018/09/16 | Accepted: 2019/02/21 | Published: 2019/02/15
Received: 2018/09/16 | Accepted: 2019/02/21 | Published: 2019/02/15
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