Machine Learning Techniques on Liver Disease - A Survey

  • Authors

    • V. V. Ramalingamdran
    • A. Pandian
    • R. Ragaven
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.23207
  • Liver Disease, Linear Regression, Support Vector Machines, Decision Tree, Random Forest, Ensemble Models.

  • Abstract

    A Liver infection causing Chronic Hepatitis B virus affects around 257 million people around the globe. About one million people who are infected from chronic infections like HBV die from chronic liver disease. Along these lines, there is a solid requirement for effective, exact and practical framework to foresee the result of such infection. It will be helpful in taking precaution steps and proper treatment. Machine learning assumes an essential job in restorative industry. Experts in machine learning today can guarantee an accurate and definite diagnosis and analysis of disease.  Machine learning methodologies have been approached on various liver disease related datasets to predict outcome result. Machine learning calculations are exceptionally useful in giving essential measurements, continuous information, and progressed examination regarding the patients' illness, "lab test results, circulatory strain, family history, clinical preliminary information, and more to" specialists. Motivation behind this paper is to give an overview on relative survey on machine learning techniques that has been used on various liver disease datasets.

     

     

  • References

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  • How to Cite

    V. Ramalingamdran, V., Pandian, A., & Ragaven, R. (2018). Machine Learning Techniques on Liver Disease - A Survey. International Journal of Engineering & Technology, 7(4.19), 485-495. https://doi.org/10.14419/ijet.v7i4.19.23207

    Received date: 2018-12-05

    Accepted date: 2018-12-05

    Published date: 2018-11-27