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Articles Published Processes
12/8/2022 9:04:30 AM | Browse: 407 | Download: 870
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Received |
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2022-06-30 06:40 |
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Peer-Review Started |
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2022-06-30 06:42 |
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To Make the First Decision |
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Return for Revision |
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2022-07-13 22:25 |
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Revised |
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2022-07-27 01:47 |
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Second Decision |
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2022-11-21 03:29 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2022-11-21 22:55 |
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Articles in Press |
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2022-11-21 22:55 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2022-11-11 02:04 |
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Typeset the Manuscript |
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2022-11-22 07:59 |
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Publish the Manuscript Online |
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2022-12-08 09:04 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Engineering, Biomedical |
| Manuscript Type |
Clinical and Translational Research |
| Article Title |
Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Surjeet Dalal, Edeh Michael Onyema and Amit Malik |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Edeh Michael Onyema, N/A, Lecturer, Department of Mathematics and Computer Science, Coal City University, Coal City University Emene, Enugu 400102, Nigeria. michael.edeh@ccu.edu.ng |
| Key Words |
Liver infection; Machine learning; Chi-square automated interaction detection; Classification and regression trees; Decision tree; XGBoost; Hyperparameter tuning |
| Core Tip |
This article proposed the hybrid eXtreme Gradient Boosting model for prediction of liver disease. This model was designed by optimizing the hyperparameter tuning with the help of Bayesian optimization. The classification and regression trees and chi-square automated interaction detection models on their own are not accurate in predicting liver disease among Indian patients. The proposed model utilized different physical health status, i.e. level of bilirubin, direct bilirubin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, total proteins, albumin, and globulin in prediction of the liver disease. This work was aimed at designing a more accurate machine learning model in liver disease prediction. |
| Publish Date |
2022-12-08 09:04 |
| Citation |
Dalal S, Onyema EM, Malik A. Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World J Gastroenterol 2022; 28(46): 6551-6563 |
| URL |
https://www.wjgnet.com/1007-9327/full/v28/i46/6551.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v28.i46.6551 |
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