ew 18: e10

Research Article

Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm

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  • @ARTICLE{10.4108/eai.13-7-2018.164177,
        author={D. Devikanniga and Arulmurugan Ramu and Anandakumar Haldorai},
        title={Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={4},
        keywords={Crow search algorithm, liver disease, sequential minimal optimization, support vector machine},
        doi={10.4108/eai.13-7-2018.164177}
    }
    
  • D. Devikanniga
    Arulmurugan Ramu
    Anandakumar Haldorai
    Year: 2020
    Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
    EW
    EAI
    DOI: 10.4108/eai.13-7-2018.164177
D. Devikanniga1,*, Arulmurugan Ramu1, Anandakumar Haldorai2
  • 1: Assistant Professor, Presidency University, Bengaluru-560064, Karnataka, India
  • 2: Associate Professor, Sri Eshwar College of Engineering, Coimbatore-641202, Tamil Nadu, India
*Contact email: mail4kanniga@gmail.com

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%.