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©The Author(s) 2024.
World J Gastroenterol. Jun 21, 2024; 30(23): 2991-3004
Published online Jun 21, 2024. doi: 10.3748/wjg.v30.i23.2991
Published online Jun 21, 2024. doi: 10.3748/wjg.v30.i23.2991
Figure 3 Signature variables of unplanned reoperation screened by machine learning.
A: Extreme gradient boosting filtered unplanned reoperation feature variable; B: Supported vector machine filtered unplanned reoperation feature variable; C: Lasso filtered unplanned reoperation feature variable; D: Feature variables common to all three learning models Venn plot. TNM: Tumor-node-metastasis; BMI: Body mass index; ASA: American Society of Anesthesiologists; PNI: Prognostic nutritional index; SVM: Supported vector machine.
- Citation: Cai LQ, Yang DQ, Wang RJ, Huang H, Shi YX. Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer. World J Gastroenterol 2024; 30(23): 2991-3004
- URL: https://www.wjgnet.com/1007-9327/full/v30/i23/2991.htm
- DOI: https://dx.doi.org/10.3748/wjg.v30.i23.2991