Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System

  • Authors:

  • Abdurrahim Akgundogdu;Serkan Kurt;Niyazi Kilic;Osman N. Ucan;Nilgun Akalin

  • Affiliations:

  • Electrical---Electronics Department, Engineering Faculty, Istanbul University, Istanbul, Turkey;Department of Industrial Electronics, Vocational School, Yildiz Technical University, Istanbul, Turkey;Electrical---Electronics Department, Engineering Faculty, Istanbul University, Istanbul, Turkey;Electrical---Electronics Department, Engineering Faculty, Istanbul University, Istanbul, Turkey;Cerrahpasa Faculty of Medicine, Istanbul University, Istanbul, Turkey

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).