©The Author(s) 2025.
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 111163
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111163
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111163
Table 3 Patient information the in training and validation sets, mean ± SD/n (%)
| Variables | Training set (n = 362) | Validation set (n = 152) | ||
| N group (n = 270) | D group (n = 92) | N group (n = 118) | D group (n = 34) | |
| Elderly (%) | 179 (66.3) | 72 (78.3) | 77 (65.3) | 28 (82.4) |
| Sex (%) | ||||
| Male | 205 (75.9) | 68 (73.9) | 80 (67.8) | 22 (64.7) |
| Female | 65 (24.1) | 24 (26.1) | 38 (32.2) | 12 (35.3) |
| Absolute value of lymphocytes, 109/L | 3.6 ± 2.6 | 3.1 ± 2.3 | 3.4 ± 2.6 | 2.7 ± 2.0 |
| White blood cell count, 109/L | 6.7 ± 2.8 | 7.1 ± 4.7 | 6.8 ± 2.6 | 6.9 ± 2.7 |
| Duration of abdominal drainage, day | 11.3 ± 3.7 | 17.8 ± 5.0 | 10.9 ± 3.9 | 18.5 ± 5.3 |
- Citation: An Y, Sun YG, Feng S, Wang YS, Chen YY, Jiang J. Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms. World J Gastrointest Oncol 2025; 17(10): 111163
- URL: https://www.wjgnet.com/1948-5204/full/v17/i10/111163.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v17.i10.111163