{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T11:15:05Z","timestamp":1792754105277,"version":"4.5.0"},"reference-count":29,"publisher":"Korean Cancer Association","issue":"3","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100003645","name":"National Cancer Center","doi-asserted-by":"publisher","award":["2111060"],"award-info":[{"award-number":["2111060"]}],"id":[{"id":"10.13039\/501100003645","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["e-crt.org"],"crossmark-restriction":true},"short-container-title":["Cancer Res Treat"],"published-print":{"date-parts":[[2025,7,15]]},"abstract":"<jats:p>PurposeGastric cancer (GC) prediction models hold potential for enhancing early detection by enabling the identification of high-risk individuals, facilitating personalized risk-based screening, and optimizing the allocation of healthcare resources. Materials and MethodsIn this study, we developed a machine learning-based GC prediction model utilizing data from the Korean National Health Insurance Service, encompassing 10,515,949 adults who had not been diagnosed with GC and underwent GC screening during 2013-2014, with a follow-up period of 5 years. The cohort was divided into training and test datasets at an 8:2 ratio, and class imbalance was mitigated through random oversampling.ResultsAmong various models, logistic regression demonstrated the highest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.708, which was consistent with the AUC obtained in external validation (0.669). Importantly, the outcomes were robust to missing data imputation and variable selection. The SHapley Additive exPlanations (SHAP) algorithm enhanced the explainability of the model, identifying advancing age, being male, Helicobacter pylori infection, current smoking, and a family history of GC as key predictors of elevated risk. ConclusionThis predictive model could significantly contribute to the early identification of individuals at elevated risk for GC, thereby enabling the implementation of targeted preventive strategies. Furthermore, the integration of noninvasive and cost-effective predictors enhances the clinical utility of the model, supporting its potential application in routine healthcare settings.<\/jats:p>","DOI":"10.4143\/crt.2024.843","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T01:20:31Z","timestamp":1734312031000},"page":"821-829","update-policy":"https:\/\/doi.org\/10.4143\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations"],"prefix":"10.4143","volume":"57","author":[{"given":"Bomi","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung Ho","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae 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