A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.

Autor:	Park B; Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea., Kim CH; Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Korea., Jun JK; National Cancer Control Institute, National Cancer Center, Goyang, Korea., Suh M; National Cancer Control Institute, National Cancer Center, Goyang, Korea., Choi KS; National Cancer Control Institute, National Cancer Center, Goyang, Korea., Choi IJ; Division of Gastroenterology, Department of Internal Medicine, Center for Gastric Cancer, National Cancer Center, Goyang, Korea., Oh HJ; Division of Gastroenterology, Department of Internal Medicine, Center for Cancer Prevention and Detection, National Cancer Center, Goyang, Korea.
Jazyk:	angličtina
Zdroj:	Cancer research and treatment [Cancer Res Treat] 2024 Dec 16. Date of Electronic Publication: 2024 Dec 16.
DOI:	10.4143/crt.2024.843
Abstrakt:	Purpose: Gastric 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 Methods: In 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 at least five years. The cohort was divided into training and test datasets at an 8:2 ratio, and class imbalance was mitigated through random oversampling. Results: Among 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. Conclusion: This predictive model could significantly contribute to the early identification of individuals at elevated risk for gastric cancer, 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.
Databáze:	MEDLINE
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