Copyright
©The Author(s) 2021.
World J Clin Cases. Dec 26, 2021; 9(36): 11255-11264
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Table 2 Model performance (Concordance-index, Brier score, and area under the curve)
| Machine learning models | Concordance-index | Brier score | AUC |
| Logistic regression | 0.84 | 0.078 | 0.85 |
| Support vector machine | 0.86 | 0.083 | 0.90 |
| Random forest | 0.86 | 0.076 | 0.92 |
| Extreme gradient boosting | 0.80 | 0.083 | 0.87 |
| Decision tree | 0.83 | 0.085 | 0.90 |
- Citation: Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264
- URL: https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v9.i36.11255