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Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures jus...

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Autores principales: Naseem, Rashid, Khan, Bilal, Shah, Muhammad Arif, Wakil, Karzan, Khan, Atif, Alosaimi, Wael, Uddin, M. Irfan, Alouffi, Badar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787853/
https://www.ncbi.nlm.nih.gov/pubmed/33489060
http://dx.doi.org/10.1155/2020/6680002
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author Naseem, Rashid
Khan, Bilal
Shah, Muhammad Arif
Wakil, Karzan
Khan, Atif
Alosaimi, Wael
Uddin, M. Irfan
Alouffi, Badar
author_facet Naseem, Rashid
Khan, Bilal
Shah, Muhammad Arif
Wakil, Karzan
Khan, Atif
Alosaimi, Wael
Uddin, M. Irfan
Alouffi, Badar
author_sort Naseem, Rashid
collection PubMed
description In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.
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spelling pubmed-77878532021-01-22 Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome Naseem, Rashid Khan, Bilal Shah, Muhammad Arif Wakil, Karzan Khan, Atif Alosaimi, Wael Uddin, M. Irfan Alouffi, Badar J Healthc Eng Research Article In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed. Hindawi 2020-12-12 /pmc/articles/PMC7787853/ /pubmed/33489060 http://dx.doi.org/10.1155/2020/6680002 Text en Copyright © 2020 Rashid Naseem et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Naseem, Rashid
Khan, Bilal
Shah, Muhammad Arif
Wakil, Karzan
Khan, Atif
Alosaimi, Wael
Uddin, M. Irfan
Alouffi, Badar
Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title_full Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title_fullStr Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title_full_unstemmed Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title_short Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome
title_sort performance assessment of classification algorithms on early detection of liver syndrome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787853/
https://www.ncbi.nlm.nih.gov/pubmed/33489060
http://dx.doi.org/10.1155/2020/6680002
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