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©The Author(s) 2025.
World J Gastrointest Oncol. May 15, 2025; 17(5): 103804
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103804
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103804
Table 4 Classification of the used evaluation indicators into types of survival from the lowest to the highest
Evaluation method | OS | CSS | DFS | |||
Min (%) | Max (%) | Min (%) | Max (%) | Min (%) | Max (%) | |
AUC | 66.90 | 98.00 | 92.00 | 96.00 | 71.00 | 85.60 |
C-index | 63.00 | 0.84.00 | - | - | 65.40 | 71.00 |
Brier-score | 13.70 | 0.25.00 | - | - | - | - |
Accuracy | 89.10 | 0.92.00 | - | - | - | - |
Specificity | 87.15 | 0.90.00 | - | - | - | - |
Sensitivity | 89.42 | 0.94.00 | - | - | - | - |
F1-score | 90.80 | 92.00 | - | - | - | - |
IBS | 14.20 | 15.10 | - | - | - | - |
- Citation: Wang HN, An JH, Wang FQ, Hu WQ, Zong L. Predicting gastric cancer survival using machine learning: A systematic review. World J Gastrointest Oncol 2025; 17(5): 103804
- URL: https://www.wjgnet.com/1948-5204/full/v17/i5/103804.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v17.i5.103804