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
Figure 1 The flow diagram of the research process.
LOS: Length of stay; LR: Logistic regression; DT: Decision tree; RF: Random forest; SVC: Support vector classifier; NB: Naïve bayes; KNN: K-nearest neighbour; XGB: eXtreme Gradient Boosting; ANN: Artificial neural network; ROC: Receiver operating characteristic; AUC: area under the receiver operating characteristic curve.
- 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