BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
1/12/2024 10:36:11 AM | Browse: 188 | Download: 610
 |
Received |
|
2023-08-24 00:40 |
|
Peer-Review Started |
|
2023-08-24 00:42 |
 |
To Make the First Decision |
|
|
|
Return for Revision |
|
2023-11-09 02:28 |
|
Revised |
|
2023-11-25 12:18 |
|
Second Decision |
|
2023-12-18 00:08 |
|
Accepted by Journal Editor-in-Chief |
|
|
|
Accepted by Executive Editor-in-Chief |
|
2023-12-25 05:48 |
|
Articles in Press |
|
2023-12-25 05:48 |
|
Publication Fee Transferred |
|
|
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2024-01-08 01:55 |
|
Publish the Manuscript Online |
|
2024-01-12 10:36 |
| ISSN |
1948-9358 (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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| 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 Study |
| Article Title |
Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Sha-Sha Cai, Teng-Ye Zheng, Kang-Yao Wang and Hui-Ping Zhu |
| ORCID |
|
| Funding Agency and Grant Number |
|
| Corresponding Author |
Hui-Ping Zhu, MM, Associate Chief Physician, Reader in Health Technology Assessment, Department of Nephrology, The First People’s Hospital of Wenling, No. 333 Chuan’an South Road, Chengxi Street, Wenling 317500, Zhejiang Province, China. zhuhuiping2261@163.com |
| Key Words |
Type 2 diabetes mellitus; Diabetic nephropathy; Random forest; Decision-making tree; Nomogram; Forecast |
| Core Tip |
Machine learning is widely used in medical prediction models. Logistic regression (nomogram), decision tree, and random forest models are three important machine learning techniques. However, few studies have compared the predictive efficacies of these three models in patients with type 2 diabetes mellitus and diabetic nephropathy. Here, we established three risk prediction models-nomogram, decision tree, and random forest-for comparison and found that random forest has the strongest combined predictive power. |
| Publish Date |
2024-01-12 10:36 |
| Citation |
Cai SS, Zheng TY, Wang KY, Zhu HP. Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus. World J Diabetes 2024; 15(1): 43-52 |
| URL |
https://www.wjgnet.com/1948-9358/full/v15/i1/43.htm |
| DOI |
https://dx.doi.org/10.4239/wjd.v15.i1.43 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
