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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 111163
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111163
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111163
Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms
Yan An, Yin-Gui Sun, Shuo Feng, Yun-Sheng Wang, Yuan-Yuan Chen, Jun Jiang, Affiliated Hospital of Shandong Second Medical University (Clinical Medical College), Weifang 261000, Shandong Province, China
Co-corresponding authors: Yuan-Yuan Chen and Jun Jiang.
Author contributions: Sun YG, Feng S, Wang YS, Chen YY and Jiang J contributed to material preparation, data collection and analysis; An Y contributed to the first draft of the manuscript; An Y, Sun YG, Feng S, Wang YS, Chen YY, Jiang J contributed to the study conception and design, they commented on previous versions of the manuscript; All authors have read and approve the final manuscript.
Supported by the Shandong Province Traditional Chinese Medicine Technology Project, No. Q-2023147; the Weifang Health Commission Research Project, No. WFWSJK-2023-033; the Weifang City Science and Technology Development Plan (Medical Category), No. 2023YX057; the Weifang Medical University 2022 Campus Level Education and Teaching Reform and Research Project, No. 2022YB051; Norman Bethune Public Welfare Foundation, No. ezmr2023-037; and Special Research Project on Optimized Management of Acute Pain, Wu Jieping Medical Foundation.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Affiliated Hospital of Weifang Medical University (No. wyfy-2024-ky-141).
Informed consent statement: As this work is a retrospective study, the ethics committee waived the requirement for patient informed consent.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data are available.
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/
Corresponding author: Jun Jiang, MD, Doctor, Affiliated Hospital of Shandong Second Medical University (Clinical Medical College), Kuiwen District, Weifang 261000, Shandong Province, China. fyjiangjun@sdsmu.edu.cn
Received: June 25, 2025
Revised: July 16, 2025
Accepted: September 1, 2025
Published online: October 15, 2025
Processing time: 112 Days and 6.1 Hours
Revised: July 16, 2025
Accepted: September 1, 2025
Published online: October 15, 2025
Processing time: 112 Days and 6.1 Hours
Core Tip
Core Tip: Delayed wound healing after gastric cancer surgery poses a significant risk to patient recovery. This study developed machine learning models specifically decision tree, support vector machine, and logistic regression for predicting postoperative delayed wound healing. The decision tree model demonstrated the best performance, achieving an accuracy of 90.1% and an area under the curve of 0.951 in the validation set. Key predictors included duration of abdominal drainage and preoperative white blood cell count. These findings offer clinicians a data-driven tool for early identification of high-risk patients and improved perioperative management.