A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

Polat, Kemal; Güneş, Salih; Arslan, Ahmet

doi:10.1016/j.eswa.2006.09.012

Cited by 257 publications

(121 citation statements)

References 5 publications

(4 reference statements)

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“…Embedding a FIS in the general structure of an artificial neural network (ANN) has the benefit of using ANN training methods to find the parameters of a fuzzy system. Linear discriminant analysis (LDA) is used to separate the two types of feature variables in a given dataset [44]. Dogantekin et al used LDA along with artificial neuro FIS (ANFIS) for the detection of diabetes [45].…”

Section: Neuro-fuzzy Inference Systems (Nfis)mentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

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Section: Neuro-fuzzy Inference Systems (Nfis)mentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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show abstract

“…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%

“…The techniques used provide much lower computation time, enhanced classification accuracy by the process to reduce noise, reduced computational cost, more transparent and comprehensible by removing insignificant features from the dataset. Polat et al (2008) proposed uses a new approach of a hybrid combination of Generalised discriminant analysis (GDA) and least square support vector machine (LS-SVM) for the classification of diabetes disease. Here the methodology is implemented in two stages: in the first stage pre-processing of the data is done using the GDA such that the discrimination between healthy and patient disease can be done.…”

Section: Related Workmentioning

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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“…There is no doubt that evaluation of data taken from patient and decisions of experts are the most important factors in diagnosis. But, this is not easy considering the number of factors that has to evaluate [6]. To help the experts and helping possible errors that can be done because of fatigued or inexperienced expert to be minimized, classification systems provide medical data to be examined in shorter time and more detailed.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model to Diabetes Using Artificial Metaplasticity

Marcano-Cedeño

Torres

Andina

2011

New Challenges on Bioinspired Applications

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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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A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

Cited by 257 publications

References 5 publications

Computational Intelligence in Early Diabetes Diagnosis: A Review

Computational Intelligence in Early Diabetes Diagnosis: A Review

GA_RBF NN: a classification system for diabetes

A Prediction Model to Diabetes Using Artificial Metaplasticity

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