Copyright
©The Author(s) 2025.
World J Gastrointest Surg. Apr 27, 2025; 17(4): 103696
Published online Apr 27, 2025. doi: 10.4240/wjgs.v17.i4.103696
Published online Apr 27, 2025. doi: 10.4240/wjgs.v17.i4.103696
Figure 3 Receiver operating characteristic curves to predict postoperative death in patients who underwent abdominal surgery.
A: The nomogram (training cohort); B: The nomogram (validation cohort); C: The decision-tree model (training cohort); D: The decision-tree model (validation cohort); E: The random-forest model (training cohort); F: The random-forest model (validation cohort); G: The gradient-boosting tree model (training cohort); H: The gradient-boosting tree model (validation cohort); I: The support vector machine model (training cohort); J: The support vector machine model (validation cohort); K: The naive Bayesian model (training cohort); L: The naive Bayesian model (validation cohort). AUC: Area under the receiver operating characteristic curve; CI: Confidence interval.
- Citation: Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. World J Gastrointest Surg 2025; 17(4): 103696
- URL: https://www.wjgnet.com/1948-9366/full/v17/i4/103696.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v17.i4.103696