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Liver Disease Detection: A Review of Machine Learning Algorithms and Scope of Optimization

Liver Disease Detection: A Review of Machine Learning Algorithms and Scope of Optimization

Aritra Pan, Shameek Mukhopadhyay, Subrata Samanta
Copyright: © 2022 |Volume: 17 |Issue: 1 |Pages: 22
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799878247|DOI: 10.4018/ijhisi.316666
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MLA

Pan, Aritra, et al. "Liver Disease Detection: A Review of Machine Learning Algorithms and Scope of Optimization." IJHISI vol.17, no.1 2022: pp.1-22. http://doi.org/10.4018/ijhisi.316666

APA

Pan, A., Mukhopadhyay, S., & Samanta, S. (2022). Liver Disease Detection: A Review of Machine Learning Algorithms and Scope of Optimization. International Journal of Healthcare Information Systems and Informatics (IJHISI), 17(1), 1-22. http://doi.org/10.4018/ijhisi.316666

Chicago

Pan, Aritra, Shameek Mukhopadhyay, and Subrata Samanta. "Liver Disease Detection: A Review of Machine Learning Algorithms and Scope of Optimization," International Journal of Healthcare Information Systems and Informatics (IJHISI) 17, no.1: 1-22. http://doi.org/10.4018/ijhisi.316666

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Abstract

In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It uses measures like precision and balanced accuracy to choose the most efficient classification algorithm in Indian 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, respectively. The study proposes a framework for highly accurate automated disease detection in the medical industry and helps in strategizing preventive measures for patients.