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

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

Mohammed Alghobiri, Hikmat Ullah Khan, Ahsan Mahmood
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 12
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.20211001.oa10
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MLA

Alghobiri, Mohammed, et al. "An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction." IJHISI vol.16, no.4 2021: pp.1-12. http://doi.org/10.4018/IJHISI.20211001.oa10

APA

Alghobiri, M., Khan, H. U., & Mahmood, A. (2021). An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-12. http://doi.org/10.4018/IJHISI.20211001.oa10

Chicago

Alghobiri, Mohammed, Hikmat Ullah Khan, and Ahsan Mahmood. "An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-12. http://doi.org/10.4018/IJHISI.20211001.oa10

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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%.