SVM ranking with backward search for feature selection in type II diabetes databases

Sarojini, B.; Narayanaswamy, Ramaier; Nickolas, S.; Samikannu, Rita

doi:10.1109/icsmc.2008.4811692

Cited by 37 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studies in the literature show no diagnosis method with a satisfactory success rate except Balakrishnan et al . (). The SVM method named ‘Weighted Least Squares Support Vector Machines (WLS‐SVM)’ used in the paper by Balakrishnan et al .…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

A new adaptive support vector machine for diagnosis of diseases

Gürbüz

Kılıç

2013

Expert Systems

View full text Add to dashboard Cite

Automatic disease diagnosis systems have been used to treat diseases for many years. The data used in the construction of these systems require correct classification. Therefore, previous literature has proposed a variety of methods. This paper develops a general‐purposed, fast and adaptive automatic disease diagnosis system, using the information generated by the newly‐designed classifier, which makes decisions with a simple rule base comprising the rules in ‘if‐then’ form. This newly‐proposed methodology is based on the support vector machine (SVM), a powerful classification algorithm. In the proposed method of this study, we added a feature of adaptivity to an SVM. In order to increase the success rate and decrease the decision‐making time, the bias value of the standard SVM is changed in an adaptive structure. This process introduces a new kind of SVM, ‘adaptive SVM’, seeking a diagnosis of diseases in a more successful way. During the training and test operations of this newly designed system, we used diabetes and breast cancer datasets, acquired from the medical database of California University. This newly proposed methodology has 100% correct classification rates on both diabetes and breast cancer datasets.

show abstract

Section: Introductionmentioning

confidence: 97%

“…The SVM method named ‘Weighted Least Squares Support Vector Machines (WLS‐SVM)’ used in the paper by Balakrishnan et al . () is a special method only for diagnosing diabetes, and it can acquire this success rate after 359 iterations.…”

Section: Introductionmentioning

confidence: 99%

A new adaptive support vector machine for diagnosis of diseases

Gürbüz

Kılıç

2013

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Embedded methods, however, integrate feature selection into the construction of classification models. Recursive feature elimination support vector machine (SVM-RFE) is a widely used technique for analysis of microarray data [9, 10]. The SVM-RFE procedure constructs a classification model using all available features, and the least informative features for that particular model are eliminated.…”

Section: Introductionmentioning

confidence: 99%

Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework

Assawamakin

Prueksaaroon

Kulawonganunchai

et al. 2013

BioMed Research International

View full text Add to dashboard Cite

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

show abstract

“…In this paper [25], proposed a Backward Search feature selection approach for finding an optimum feature subset that enhances the classification accuracy of Naive Bayes and SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

Hashi¹,

Zaman²,

Hasan³

2017

IJCA

View full text Add to dashboard Cite

Data mining techniques are being used extensively in healthcare sector to discover hidden pattern and relationship between patients' record and their medical diagnosis dataset. In the concept of disease prediction, high classification accuracy can be obtained from accurately pre-processed and trained model. But existence of unimportant and irrelevant attributes in the training dataset may decrease the predictive accuracy and increase the time complexity in training phase. To increase the accuracy and efficiency, feature selection technique is frequently used in data mining. In this paper, a sequential forward selection based wrapper approach is proposed to select optimal and informative feature subset. It is known that diabetes mellitus is the most serious health problem and the complications lead to cause of death. So the aim of this research is to identify the significant attributes and classify diabetes dataset. The proposed approach is used to build the classifier models like Decision tree, K-Nearest Neighbor and Support Vector Machine produces the accuracies of 81.17%, 86.36% and 87.01% respectively. Finally, from results it is clear that the proposed model is performing better with high accuracy comparing the similar existing models. In the research, the Pima Indian diabetes dataset is used.

show abstract

SVM ranking with backward search for feature selection in type II diabetes databases

Cited by 37 publications

References 14 publications

A new adaptive support vector machine for diagnosis of diseases

A new adaptive support vector machine for diagnosis of diseases

Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework

Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

Contact Info

Product

Resources

About