Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Supantha Das, A. Hazra, Soumen Kumar Pati, Soumadip Ghosh, Saurav Mallik, Suharta Banerjee, Ayan Mukherji, Aimin Li, Zhongming Zhao
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Abstract

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.
基于神经模糊规则分类器的慢性肾脏疾病检测
在当今世界,慢性肾脏疾病(CKD)与癌症一样严重。它甚至可能导致肾脏永久性衰竭。为了及时治疗,需要对这种疾病进行初步发现。我们的工作提出了一个分类器(命名为ANFIS),按照神经模糊的概念,以检测慢性肾脏疾病的存在。我们在研究中使用了几位患者的血液检测结果。我们将所提出的分类器与传统的分类器如多层感知器、支持向量机、逻辑回归和决策树进行了比较。实验结果表明,我们提出的基于神经模糊规则的分类器比本文使用的其他分类器性能更好。与其他分类器相比,ANFIS的准确率提高了3%到4%。
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