An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Polat, Kemal; Güneş, Salih

doi:10.1016/j.dsp.2006.09.005

Cited by 324 publications

(151 citation statements)

References 5 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%

“…ROC is defined as a plot of test or relationship between Sensitivity or True Positive Rate (TPR) as the Y coordinate and 1-Specificity or False Positive Rate (FPR) as the X coordinate Confusion Matrix: A confusion matrix (Polat and Gunes, 2007;Polat et al, 2008) contains information regarding actual and predicted classifications done by a classification system. Kappa statistics: It is defined as performance to measure the true classification or accuracy of the algorithm.…”

Section: Noof Correctly Classified Instances Recall Total Noof Instmentioning

confidence: 99%

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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%

Section: Noof Correctly Classified Instances Recall Total Noof Instmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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“…Degani, Rosanna [4] used fuzzy methodologies in computerized ECG diagnosis and later on Warren, Jim [16] used fuzzy logic in designing a clinical decision support system. Jena, R.K. [6] produced soft computing methodologies in Bioinformatics, Polat, K. and Gϋne S. [11] developed an expert system approach based on principle component analysis and adopted neuro-fuzzy inference system to diagnosis of diabetes. Baskaran, A. et al [1] made a study on modeling and automation of diagnosis and treatment of diabetes.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Diagnostic System for Diabetes

Srivastava¹,

Sharma²,

Singh³

2012

IJCA

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In the present paper, we propose a soft computing diagnostic system for detecting different phases of diabetes. The proposed system is user friendly and will guide patients to evolve proper strategies so that they could maintain their blood sugar level by adopting suitable life style. The proposed system not only acts as a referral system in between patient and medical expert but also sharpen the diagnostic process of medical experts. A number of cases on the basis of available clinical datas have been investigated to check the validity of system.

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“…Expert systems and different artificial intelligence techniques for classification systems in medical diagnosis is increasing gradually. As for other clinical diagnosis problems, classification systems have been used for diabetes diagnosis problem [7].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model to Diabetes Using Artificial Metaplasticity

Marcano-Cedeño

Torres

Andina

2011

Lecture Notes in Computer Science

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Abstract. Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers,that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%.

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An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Cited by 324 publications

References 5 publications

GA_RBF NN: a classification system for diabetes

GA_RBF NN: a classification system for diabetes

Soft Computing Diagnostic System for Diabetes

A Prediction Model to Diabetes Using Artificial Metaplasticity

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