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Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds

Author

Listed:
  • Aritra Pan

    (Indian Institute of Management, Bodh Gaya, India)

  • Shameek Mukhopadhyay

    (The Heritage Academy, Kolkata, India)

  • Subrata Samanta

    (Ernst and Young, India)

Abstract

Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease.Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormone,enzymes,age,bloodcounts,etc.This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques.The article proposes an enhanced framework on the original study by Ramana et al. (2011).It uses evaluation measures like Precision and Balanced Accuracy to choose the most efficient classification algorithm in INDIA and USA patient datasets using various factors like enzymes,age,etc.Using Youden’s Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity.A framework is proposed for highly accurate automated disease detection in the medical industry,and it helps in strategizing preventive measures for patients with liver diseases.

Suggested Citation

  • Aritra Pan & Shameek Mukhopadhyay & Subrata Samanta, 2022. "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.
    • Handle: RePEc:igg:jhisi0:v:17:y:2022:i:2:p:1-19
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. 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.
    3. P Priyanga & N C. Naveen, 2018. "Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 13(4), pages 82-97, October.
    4. Kauser Ahmed P. & Debi Prasanna Acharjya, 2021. "Knowledge Inferencing Using Artificial Bee Colony and Rough Set for Diagnosis of Hepatitis Disease," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(2), pages 49-72, April.
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