Construction of a prediction model for postoperative survival of pancreatic cancer based on SMOTE-ENN combined with XGBoost algorithm

Corresponding author: GuoYarong, gyr5258@126.com

DOI: 10.12201/bmr.202506.00058

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  Abstract: Purpose Different algorithms were used to build a prediction model for survival outcomes of patients after pancreatic cancer surgery based on the new version of AJCC staging and large-scale data.Methods? Based on the SEER database, SMOTE and SMOTE-ENN algorithms are used to process unbalanced data, LR, RF, SVM, DT, and XGBoost algorithms are used to build and compare prognostic models, and SHAP is introduced to interpret the models.Results? The performance of SMOTE-ENN combined with XGBoost model was the best (accuracy rate was 0.862, precision rate was 0.952, recall rate was 0.712, F1 value was 0.762, AUC value was 0.884, and Brier score was 0.108). The calibration curve and decision curve showed that the model had good calibration effect and high clinical application value respectively.Conclusion? The XGBoost model has the best performance and can be used as a new high-performance postoperative prognosis prediction model under AJCC staging that conforms to the current clinical staging system, providing theoretical support for predicting postoperative patient survival outcomes and formulating personalized treatment plans.

  Key words: pancreatic cancer; imbalanced data; XGBoost; outcome prediction

  Submit time: 23 June 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-06-01	10.12201/bmr.202506.00058V1	Download

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