Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/1786
Title: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
Authors: D. Devikanniga
Arulmurugan Ramu
Anandakumar Haldorai
Issue Date: 2020
Publisher: European Alliance for Innovation
Citation: EAI Endorsed Transactions on Energy Web
Abstract: The early and accurate prediction of liver disease in patients is still a challenging task among medical practitioners even with latest advanced technologies. The support vector machines are widely used in medical domain. It has proved its efficiency on producing good diagnostic parameters. These results can be further improved by optimizing the hyperparameters of support vector machines. The proposed work is based on optimizing support vector machines with crow search algorithm. This optimized support vector machine classifier (CSA-SVM) is used for accurate diagnosis of Indian liver disease data. The various similar state of art algorithms are taken for comparison with proposed approach to prove its efficient. The performance of CSA-SVM is found to be outstanding among all other approaches in terms of all metrics taken for comparison. It has yielded the classification accuracy of 99.49%.
URI: http://localhost:8080/xmlui/handle/123456789/1786
Appears in Collections:Computer Science Engineering Department

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