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Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease

Author

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  • Sakshi Takkar

    (Lovely Professional University, Phagwara, India)

  • Aman Singh

    (Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India)

  • Babita Pandey

    (Department of Computer Applications, Lovely Professional University, Phagwara, India)

Abstract

Liver diseases represent a major health burden worldwide. Machine learning (ML) algorithms have been extensively used to diagnose liver disease. This study accordingly aims to employ various individual and integrated ML algorithms on distinct liver disease datasets for evaluating the diagnostic performances, to integrate dimensionality reduction method with the ML algorithms for analyzing variation in results, to find the best classification model and to analyze the merits and demerits of these algorithms. KNN and PCA-KNN emerged to be the top individual and integrated models. The study also concluded that one specific algorithm can't show best results for all types of datasets and integrated models not always perform better than the individuals. It is observed that no algorithm is perfect and performance of an algorithm totally depends on the dataset type and structure, its number of observations, its dimensions and the decision boundary.

Suggested Citation

  • Sakshi Takkar & Aman Singh & Babita Pandey, 2017. "Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 8(4), pages 38-60, October.
    • Handle: RePEc:igg:jehmc0:v:8:y:2017:i:4:p:38-60
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.2017100103
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    Cited by:

    1. Aritra Pan & Shameek Mukhopadhyay & Subrata Samanta, 2022. "RETRACTED: Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(2), pages 1-19, April.

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