Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression
Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neu...
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doaj-dd46ea5606da4819aadde5184dd1eb252020-11-24T20:49:58ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-12-01910.3389/fphys.2018.01753415599Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease ProgressionAli Yadollahpour0Jamshid Nourozi1Seyed Ahmad Mirbagheri2Eric Simancas-Acevedo3Francisco R. Trejo-Macotela4Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranDepartment of Environmental and Energy, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, IranTelematics Engineering Department, Polytechnic University of Pachuca, Zempoala, MexicoGraduate and Research Department, Polytechnic University of Pachuca, Zempoala, MexicoBackground and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed.https://www.frontiersin.org/article/10.3389/fphys.2018.01753/fullchronic kidney diseaseadaptive neuro fuzzy inference systemmedical decision support systemrenal failure progressionprediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Yadollahpour Jamshid Nourozi Seyed Ahmad Mirbagheri Eric Simancas-Acevedo Francisco R. Trejo-Macotela |
spellingShingle |
Ali Yadollahpour Jamshid Nourozi Seyed Ahmad Mirbagheri Eric Simancas-Acevedo Francisco R. Trejo-Macotela Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression Frontiers in Physiology chronic kidney disease adaptive neuro fuzzy inference system medical decision support system renal failure progression prediction |
author_facet |
Ali Yadollahpour Jamshid Nourozi Seyed Ahmad Mirbagheri Eric Simancas-Acevedo Francisco R. Trejo-Macotela |
author_sort |
Ali Yadollahpour |
title |
Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression |
title_short |
Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression |
title_full |
Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression |
title_fullStr |
Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression |
title_full_unstemmed |
Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression |
title_sort |
designing and implementing an anfis based medical decision support system to predict chronic kidney disease progression |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physiology |
issn |
1664-042X |
publishDate |
2018-12-01 |
description |
Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed. |
topic |
chronic kidney disease adaptive neuro fuzzy inference system medical decision support system renal failure progression prediction |
url |
https://www.frontiersin.org/article/10.3389/fphys.2018.01753/full |
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