Electrical-Electronics Department, Istanbul University, Avcilar, Istanbul, Turkey.
J Med Syst. 2010 Dec;34(6):1003-9. doi: 10.1007/s10916-009-9317-2. Epub 2009 May 26.
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).
自适应神经模糊推理系统（ANFIS）是过去几十年中解决函数逼近和模式识别问题的一种非常有用和强大的神经网络方法。本文使用 ANFIS 方法研究了肾衰竭疾病的诊断。总共从土耳其伊斯坦布尔大学的伊斯坦布尔和切拉帕萨医学院获得了 112 名患者的原始数据。其中 64 名与肾衰竭有关，其余数据属于健康人。在 ANFIS 模型中，选择了三个规则和高斯隶属函数，其中规则是通过减法聚类方法确定的。考虑了患者的七个参数作为系统的输入。这些是：血尿素氮（BUN）、肌酐、尿酸、钾（K）、钙（Ca）、磷（P）和年龄。我们试图判断患者是否患病。我们在 ANFIS 中取得了 100%的成功率，并且比支持向量机（SVM）和人工神经网络（ANN）的结果更好。
