A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing

Çomak, Emre; Polat, Kemal; Güneş, Salih; Arslan, Ahmet

doi:10.1016/j.eswa.2005.12.001

Cited by 60 publications

(32 citation statements)

References 6 publications

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“…Parameter values used for any technique are those set as default in WEKA. Also the results obtained by support vector machines algorithm [10], IncNet [43], fuzzy approach [44], FLEXNFIS [45], FNN [46], RULES-4 [47] and C4.5 [48], Naïve Bayes [49,50], BNND and BNNF methods from [51], SSVM [52], RSVM [53], SVM [54], LSSVM [55], FAIRS [56], DC-RBFNN [57], Boost [58], RIPPER [59], INB [60], and GPF [61] were used in the experiments.…”

Section: Results Of Numerical Experimentsmentioning

confidence: 99%

A New Method for Solving Supervised Data Classification Problems

Shabanzadeh

Yusof

2014

Abstract and Applied Analysis

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Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

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Section: Results Of Numerical Experimentsmentioning

confidence: 99%

A New Method for Solving Supervised Data Classification Problems

Shabanzadeh

Yusof

2014

Abstract and Applied Analysis

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“…During diagnosis of the disease, the levels of these enzymes are analysed. Because of the fact that effects of different alcohol dosages vary from one person to the other as well as the fact that there are many enzymes, there can be frequent possible errors in diagnosis [20]. BUPA Liver Disorders data set is prepared by BUPA medical research company.…”

Section: E Experiments With Real Datamentioning

confidence: 99%

Emphatic Constraints Support Vector Machine

Sabzekar¹,

Yazdi²,

Naghibzadeh³

et al. 2010

IJCEE

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“…Automated diagnostics for liver disease have been intensively researched in recent years, and many methods have been proposed and applied [3,4,1,[5][6][7][8]. Data mining (DM) techniques, such as artificial neural networks (ANN), intelligent algorithms, fuzzy sets, and support vector machines (SVMs), have recently found medical application.…”

Section: Introductionmentioning

confidence: 99%

“…Growth curve analysis using logistic regression, decision tree, and neural networks showed that liver disease was accurately diagnosed in 72.55% of the cases and that the sensitivity was 78.62% when a neural network was used [1]. In the pattern recognition literature, Comak presented a hybrid method based on combining least square support vector machines (LSSVM) with fuzzy weighting preprocessing to diagnose liver disorders using the standard BUPA Liver Disorders Dataset benchmark [5]. The highest classification accuracy yet reported for this dataset is 94.29%.…”

Section: Introductionmentioning

confidence: 99%