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GA_SVM: A Classification System for Diagnosis of Diabetes

GA_SVM: A Classification System for Diagnosis of Diabetes

Dilip Kumar Choubey, Sanchita Paul
Copyright: © 2017 |Pages: 39
ISBN13: 9781522521280|ISBN10: 1522521283|EISBN13: 9781522521297
DOI: 10.4018/978-1-5225-2128-0.ch012
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MLA

Choubey, Dilip Kumar, and Sanchita Paul. "GA_SVM: A Classification System for Diagnosis of Diabetes." Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., IGI Global, 2017, pp. 359-397. https://doi.org/10.4018/978-1-5225-2128-0.ch012

APA

Choubey, D. K. & Paul, S. (2017). GA_SVM: A Classification System for Diagnosis of Diabetes. In S. Shandilya, S. Shandilya, K. Deep, & A. Nagar (Eds.), Handbook of Research on Soft Computing and Nature-Inspired Algorithms (pp. 359-397). IGI Global. https://doi.org/10.4018/978-1-5225-2128-0.ch012

Chicago

Choubey, Dilip Kumar, and Sanchita Paul. "GA_SVM: A Classification System for Diagnosis of Diabetes." In Handbook of Research on Soft Computing and Nature-Inspired Algorithms, edited by Shishir K. Shandilya, et al., 359-397. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2128-0.ch012

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Abstract

The modern society is prone to many life-threatening diseases which if diagnosis 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. There are already several existing methods, which have been implemented for the diagnosis of diabetes. In this manuscript, firstly, Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM used for the classification of PIDD. Secondly GA used as an Attribute selection method and then used Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM on that selected attributes of PIDD for classification. So, here compared the results with and without GA in PIDD, and Linear Kernel proved better among all of the noted above classification methods. It directly seems in the paper that GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can be also used for other kinds of medical diseases.

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