Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastrointest Surg. Jul 27, 2026; 18(7): 119087
Published online Jul 27, 2026. doi: 10.4240/wjgs.v18.i7.119087
Published online Jul 27, 2026. doi: 10.4240/wjgs.v18.i7.119087
Systematic assessment of mixed imputation methods and explainable machine learning
Jia-Yi Li, Department of Medicine, Dalian University of Technology, Dalian 116024, Liaoning Province, China
Jia-Yi Li, Yan Zhao, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, Shenyang 110042, Liaoning Province, China
Author contributions: Li JY and Zhao Y contributed to this paper; Li JY designed the overall concept and outline of the manuscript; Zhao Y contributed to the discussion and design of the manuscript; Li JY and Zhao Y contributed to the writing, and editing the manuscript and review of literature. All authors have read and approved the final manuscript.
AI contribution statement: Grammarly and DeepL were used for language polishing, grammar correction, and/or translation assistance. No AI tool was used to generate research data, perform data analysis, create references, formulate scientific conclusions, or replace the authors’ intellectual input. All AI-assisted language changes were carefully reviewed, edited, and approved by the authors.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yan Zhao, MD, PhD, Chief Physician, Director, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, No. 44 Xiaoheyan Street, Dadong District, Shenyang 110042, Liaoning Province, China. drzhao@dlut.edu.cn
Received: January 19, 2026
Revised: February 1, 2026
Accepted: April 14, 2026
Published online: July 27, 2026
Processing time: 189 Days and 18.5 Hours
Revised: February 1, 2026
Accepted: April 14, 2026
Published online: July 27, 2026
Processing time: 189 Days and 18.5 Hours
Core Tip
Core Tip: This opinion review evaluates the study, which introduces a hybrid imputation framework, HDI-MF-Gower, combined with an explainable extra trees classifier for predicting postoperative survival in gastric cancer. The study’s innovative approach addresses the challenges of missing clinical data by adapting the iterative imputation method MissForest. Although the model demonstrates clinical transparency and methodological rigor, its marginal improvement over existing ensemble methods and lack of external validation highlight areas for further research in multimodal data integration and multi-institutional validation.