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An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction

Author

Listed:
  • Mohammed Alghobiri

    (King Khalid University, Abdha, Saudi Arabia)

  • Hikmat Ullah Khan

    (COMSATS University Islamabad, Pakistan)

  • Ahsan Mahmood

    (COMSATS University Islamabad, Pakistan)

Abstract

The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

Suggested Citation

  • Mohammed Alghobiri & Hikmat Ullah Khan & Ahsan Mahmood, 2021. "An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-12, October.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-12
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    References listed on IDEAS

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    1. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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