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

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

Aritra Pan, Shameek Mukhopadhyay, Subrata Samanta
Copyright: © 2022 |Volume: 17 |Issue: 2 |Pages: 19
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799878254|DOI: 10.4018/IJHISI.299956
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MLA

Pan, Aritra, et al. "Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds." IJHISI vol.17, no.2 2022: pp.1-19. http://doi.org/10.4018/IJHISI.299956

APA

Pan, A., Mukhopadhyay, S., & Samanta, S. (2022). Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds. International Journal of Healthcare Information Systems and Informatics (IJHISI), 17(2), 1-19. http://doi.org/10.4018/IJHISI.299956

Chicago

Pan, Aritra, Shameek Mukhopadhyay, and Subrata Samanta. "Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds," International Journal of Healthcare Information Systems and Informatics (IJHISI) 17, no.2: 1-19. http://doi.org/10.4018/IJHISI.299956

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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.