Copyright
©The Author(s) 2023.
World J Orthop. Oct 18, 2023; 14(10): 741-754
Published online Oct 18, 2023. doi: 10.5312/wjo.v14.i10.741
Published online Oct 18, 2023. doi: 10.5312/wjo.v14.i10.741
Model name | Accuracy | Precision | Recall | F1 score | AUC |
LR | 0.650 | 0.643 | 0.668 | 0.655 | 0.650 |
DT | 0.606 | 0.616 | 0.552 | 0.583 | 0.605 |
RF | 0.619 | 0.618 | 0.613 | 0.616 | 0.619 |
SVM | 0.664 | 0.656 | 0.659 | 0.658 | 0.712 |
NB | 0.644 | 0.657 | 0.595 | 0.624 | 0.643 |
KNN | 0.617 | 0.611 | 0.639 | 0.625 | 0.617 |
XGB | 0.630 | 0.632 | 0.616 | 0.624 | 0.630 |
ANN | 0.606 | 0.596 | 0.645 | 0.619 | 0.606 |
- Citation: Tian CW, Chen XX, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14(10): 741-754
- URL: https://www.wjgnet.com/2218-5836/full/v14/i10/741.htm
- DOI: https://dx.doi.org/10.5312/wjo.v14.i10.741