E-Anfis to Diagnose the Progression of Chronic Kidney Disease
Subhashini R1, Jeyakumar M K2
1Subhashini R, Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, (Tamil Nadu), India.
2Dr. Jeyakumar M K, Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 526-531 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2304037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Chronic renal failure is not well explored. In this study, an artificial intelligence technique is proposed for overcoming the occurrence of local minima and local maxima in diagnosing the progression of kidney disease. An AI technique, a mixture of ALO and ANFIS, E-ANFIS (Enhanced Adaptive Neurofuzzy Inference Systems) is introduced. Normally back propagation is used in ANFIS, but in proposed using new optimizer ALO. The performance of ANFIS is improved by utilizing the Ant Lion Optimizer. This enhanced ANFIS used to diagnose the progression stage of the CKD. The proposed technique was executed in Matlab/Simulink platform and compared with the existing techniques ANFIS, fuzzy, and ANN. Performance evaluation is assessed in terms of accuracy, recall, precision, F-measure and specificity. The obtained results showed that the newly introduced E-ANFIS is the best algorithm when compared to other involved existing algorithms.
Keywords: Ant Lion Optimizer, Adaptive Neurofuzzy Inference System, data mining, E-ANFIS, GFR, microalbuminuria.
Scope of the Article: System Integration