BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
2/26/2026 10:19:37 AM | Browse: 4 | Download: 1
 |
Received |
|
2025-10-11 05:35 |
|
Peer-Review Started |
|
2025-10-11 05:37 |
|
First Decision by Editorial Office Director |
|
2025-11-19 09:59 |
|
Return for Revision |
|
2025-11-19 09:59 |
|
Revised |
|
2025-12-02 12:50 |
|
Publication Fee Transferred |
|
2025-12-09 02:30 |
|
Second Decision by Editor |
|
2026-01-08 02:47 |
 |
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2026-01-08 07:26 |
|
Articles in Press |
|
2026-01-08 07:26 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2026-02-05 03:40 |
|
Publish the Manuscript Online |
|
2026-02-26 10:19 |
| ISSN |
1948-9366 (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 |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Computer Science, Interdisciplinary Applications |
| Manuscript Type |
Retrospective Study |
| Article Title |
Development of a machine learning-based model for predicting postoperative survival in gastric cancer
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ya-Na Lü, Dong Liu, Shuai Tao, Ju Wu, Shu-Juan Yu and Hui-Ling Yuan |
| ORCID |
|
| Funding Agency and Grant Number |
|
| Corresponding Author |
Ju Wu, Department of General Surgery, Zhongshan Hospital Affiliated to Dalian University, No 6 Jiefang Street, Zhongshan District, Dalian 116001, Liaoning Province, China. wuju@s.dlu.edu.cn |
| Key Words |
Gastric cancer; Machine learning; Survival prediction; Missing data imputation; Extra trees |
| Core Tip |
This study developed a novel hybrid imputation method (HDI-MF-Gower) to handle missing clinical data. We then built and validated a robust machine learning model (extra trees classifier) for predicting postoperative 3-year survival in gastric cancer patients. The model demonstrated high performance (area under the curve of 0.853), and its clinical application is facilitated by interpretable SHapley Additive exPlanations analysis and a user-friendly online prediction tool, aiding personalized treatment planning. |
| Publish Date |
2026-02-26 10:19 |
| Citation |
Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951 |
| URL |
https://www.wjgnet.com/1948-9366/full/v18/i2/114951.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v18.i2.114951 |
© 1993-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
