A New Approach for Diagnosis of Diabetes and Prediction of Cancer Using ANFIS

Kalaiselvi, C.; Nasira, G. M.

doi:10.1109/wccct.2014.66

Cited by 51 publications

(21 citation statements)

References 16 publications

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“…The same dataset has been used in the reference (Kayaer and Yildirim, 2003;Goncalves et al, 2006;Polat and Gunes, 2007;Kahramanli and Allahverdi, 2008;Temurtas et al, 2009;Dogantekin et al, 2010;Ganji and Abadeh, 2010;Jayalakshmi and Santhakumaran, 2010;Ephzibah, 2011;Ganji and Abadeh, 2011;Kala et al, 2011;Karegowda et al, 2011;Lee, 2011;Lukka, 2011;Orkcu and Bal, 2011;Qasem and Shamsuddin, 2011;Selvakuberan et al, 2011;Karatsiolis and Schizas, 2012;Aslam et al, 2013;Das et al, 2013;Kalaiselvi and Nasira, 2014;Seera and Lim, 2014;Choubey and Paul, 2016 …”

Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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Abstract:The modern society is prone to many life-threatening diseases, which if diagnosed early, can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes disease. There are already several existing methods, which have been implemented for the diagnosis of diabetes dataset. Here, the proposed approach consists of two stages: in first stage Genetic algorithm (GA) used as an attribute (feature) selection which reduces 4 attributes among 8 attributes, and in the second stage Radial Basis Function Neural Network (RBF NN) has been used for classification on selected attributes among all the attributes. The experimental results show the performance of the proposed methodology on Pima Indian Diabetes Dataset (PIDD) and provide better classification for diagnosis of diabetes patients on PIDD. GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can also be used for other kinds of medical diseases.

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Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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“…In this paper [7]of "Diagnosis of diabetes Mellitus based on risk Factor "author Sumathy et al(2010) Proposed method that diagnosed based on risk factor .The suggested system used Artificial neural network (ANN)architecture for classification which had surprised multilayer feed forward network with back propagation learning algorithm. The ANN technique gave better result than other Existing technique.…”

Section: Related Workmentioning

confidence: 99%

Prognosis of Diabetes using Neural Network, Fuzzy Logic, Gaussian Kernel Method

Giri¹,

Todmal²

2015

IJCA

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In Today's world there is an increase in the prevalence of diabetes mellitus and therefore the disease is recognising as a major global public health problem.medical data mining extracts hidden patterns from medical data. This is to design system for diabetes prediction.. The soft computing technique is most useful and powerful technique used for diagnosis purpose. The proposed system a novel approach for diagnosis of diabetes which has two stages to predict the diabetes status. Initial Stage we are using Gaussian kernel function which help to distribution of data and second stage adopt two computational intelligence and knowledge engineering technique such as fuzzy logic and neural network. The benefit applying these is that accuracy of prediction rate will be higher than most of the suggested system for predicting the occurrence of diabetes mellitus. The dataset used for the Experimental is based on Pima Indian Dataset from University of California.

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“…Neuro-Fuzzy (NF) models incorporate the generic advantages of artificial neural networks in modeling imprecise data and qualitative knowledge as well as transmission of uncertainty. One of the most powerful types of NF systems is the Adaptive Neuro-Fuzzy Inference System (ANFIS) [24], which has shown very good learning, classification, diagnosis and prediction capabilities in different medical diseases/applications [25]- [29]. In this work a Voting-Adaptive Neuro-Fuzzy Inference System (V-ANFIS) approach is proposed to improve force estimation in a RAMIS scenario.…”

Section: Introductionmentioning

confidence: 99%

V-ANFIS for Dealing with Visual Uncertainty for Force Estimation in Robotic Surgery

Avilés¹,

Alsaleh²,

Montseny³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Accurate and robust estimation of applied forces in Robotic-Assisted Minimally Invasive Surgery is a very challenging task. Many vision-based solutions attempt to estimate the force by measuring the surface deformation after contacting the surgical tool. However, visual uncertainty, due to tool occlusion, is a major concern and can highly affect the results' precision. In this paper, a novel design of an adaptive neuro-fuzzy inference strategy with a voting step (V-ANFIS) is used to accommodate with this loss of information. Experimental results show a significant accuracy improvement from 50% to 77% with respect to other proposals.

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A New Approach for Diagnosis of Diabetes and Prediction of Cancer Using ANFIS

Cited by 51 publications

References 16 publications

GA_RBF NN: a classification system for diabetes

GA_RBF NN: a classification system for diabetes

Prognosis of Diabetes using Neural Network, Fuzzy Logic, Gaussian Kernel Method

V-ANFIS for Dealing with Visual Uncertainty for Force Estimation in Robotic Surgery

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