A comparative study on diabetes disease diagnosis using neural networks

Temurtaş, Hasan; Yumuşak, Nejat; Temurtaş, Feyzullah

doi:10.1016/j.eswa.2008.10.032

Cited by 263 publications

(118 citation statements)

References 24 publications

Supporting

Mentioning

116

Contrasting

Unclassified

Order By: Relevance

“…The best results are marked with bold. As shown, in each data classification case, the PNN models trained by means of Algorithm 1 or Algorithm 2 outperform PNNs trained using state-of-the-art methods (Georgiou et al 2006;Chang et al 2008;Georgiou et al 2008;Saiti et al 2009;Temurtas et al 2009;Chandra and Babu 2011;Yeh and Lin 2011;Azar and El-Said 2013). Our algorithms also perform better than the reference classifiers in all considered data set classification problems.…”

Section: Comparison To State-of-the-art Proceduresmentioning

confidence: 63%

“…Probabilistic neural networks have found their implementation in a variety of classification fields. It was presented in image classification and recognition (Chtioui et al 1996(Chtioui et al , 1998Ramakrishnan and Selvan 2007;Wen et al 2008), earthquake magnitude prediction (Adeli and Panakkat 2009), multiple partial discharge sources classification (Venkatesh and Gopal 2011), interval information processing (Kowalski and Kulczycki 2014) or medical diagnosis and prediction (Shan et al 2002;Folland et al 2004;Huang and Liao 2004;Temurtas et al 2009;Mantzaris et al 2011).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

Kusy

Kluska

2016

Soft Comput

View full text Add to dashboard Cite

One of the most important problems in probabilistic neural network (PNN) operation is the minimization of its structure. In this paper, two heuristic approaches of PNN's pattern layer reduction are applied. The first method is based on a k-means clustering procedure. In the second approach, the candidates for the network's pattern neurons are selected on the basis of a support vector machines algorithm. Modified models are compared in the classification problems with the traditional PNN, four well-known computational intelligence algorithms (single decision tree, multilayer perceptron, support vector machines, k-means algorithm) and PNN trained by the state-of-the-art procedures. Seven medical benchmark databases are investigated and one authors' own real ovarian cancer data set. Comparison is performed on the basis of the global performance indices which depend on the accuracy, sensitivity and specificity. These indices are computed using the standard tenfold cross-validation procedure. On the basis of the reported results, we show that the algorithm based on k-means clustering is a better PNN structure reduction procedure. Furthermore, this algorithm is much less timeconsuming.

show abstract

Section: Comparison To State-of-the-art Proceduresmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

Kusy

Kluska

2016

Soft Comput

View full text Add to dashboard Cite

show abstract

“…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%

“…The methodology is implemented on various datasets including Breast Cancer Wisconsin, PIDD, and Liver Disorders and performs better as compared to other existing techniques. Temurtas et al (2009) stated Levenberg-Marquardt (LM) algorithm and Probabilistic neural network (PNN) were used to train a multilayer neural network, tenfold cross validation technique were used for estimation of result. The used techniques LM, PNN, 10-Fold cross validation provide better correct training pattern than conventional validation method.…”

Section: Related Workmentioning

confidence: 99%

GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

View full text Add to dashboard Cite

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.

show abstract

“…Although detection of diabetes is improving, about half of the patients with Type II diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 years. Thus, earlier detection of Type II diabetes and treatment of hyperglycaemia and related to metabolic abnormalities is of vital importance [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model to Diabetes Using Artificial Metaplasticity

Marcano-Cedeño

Torres

Andina

2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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%.

show abstract

A comparative study on diabetes disease diagnosis using neural networks

Cited by 263 publications

References 24 publications

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

GA_RBF NN: a classification system for diabetes

A Prediction Model to Diabetes Using Artificial Metaplasticity

Contact Info

Product

Resources

About