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

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

Sakshi Takkar, Aman Singh, Babita Pandey
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 23
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781522513841|DOI: 10.4018/IJEHMC.2017100103
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MLA

Takkar, Sakshi, et al. "Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease." IJEHMC vol.8, no.4 2017: pp.38-60. http://doi.org/10.4018/IJEHMC.2017100103

APA

Takkar, S., Singh, A., & Pandey, B. (2017). Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease. International Journal of E-Health and Medical Communications (IJEHMC), 8(4), 38-60. http://doi.org/10.4018/IJEHMC.2017100103

Chicago

Takkar, Sakshi, Aman Singh, and Babita Pandey. "Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease," International Journal of E-Health and Medical Communications (IJEHMC) 8, no.4: 38-60. http://doi.org/10.4018/IJEHMC.2017100103

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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.

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