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Articles Published Processes
11/24/2023 9:22:47 AM | Browse: 178 | Download: 563
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Received |
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2023-08-14 02:10 |
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Peer-Review Started |
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2023-08-04 07:03 |
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To Make the First Decision |
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Return for Revision |
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2023-10-09 03:02 |
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Revised |
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2023-10-23 06:43 |
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Second Decision |
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2023-11-07 02:18 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2023-11-13 08:17 |
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Articles in Press |
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2023-11-13 08:17 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2023-11-21 01:50 |
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Publish the Manuscript Online |
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2023-11-24 09:22 |
| ISSN |
2307-8960 (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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2023. 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 |
Endocrinology & Metabolism |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Chung-Chi Yang, Chung-Hsin Peng, Li-Ying Huang, Fang Yu Chen, Chun-Heng Kuo, Chung-Ze Wu, Te-Lin Hsia and Chung-Yu Lin |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Chung-Yu Lin, MD, Doctor, Doctor, Department of Cardiology, Fu Jen Catholic University Hospital, No. 69 Guizi Road, Taishan District, New Taipei City 24352, Taiwan. a02076@mail.fjuh.fju.edu.tw |
| Key Words |
Myocardial perfusion scintigraphy; Machine learning; Type 2 diabetes; Thallium-201 |
| Core Tip |
This is a retrospective study to use four machine learning methods to evaluate the impacts of demographic and biochemistry data to identify subjects with abnormal myocardial perfusion scan in Chinese type 2 diabetes. Our results showed that gender was the most important factor, followed by body mass index, age, LDL-cholesterol, glycated hemoglobin and smoking accordingly. |
| Publish Date |
2023-11-24 09:22 |
| Citation |
Yang CC, Peng CH, Huang LY, Chen FY, Kuo CH, Wu CZ, Hsia TL, Lin CY. Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes. World J Clin Cases 2023; 11(33): 7951-7964 |
| URL |
https://www.wjgnet.com/2307-8960/full/v11/i33/7951.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v11.i33.7951 |
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