Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients

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.

Figure 2 Receiver operating characteristic curve for machine learning models in the original data. ROC: Receiver operating characteristic; 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.

Figure 3 Shapley additive explanations summary plots of each model. A: Logistic regression; B: Decision tree; C: Random forest; D: Support vector classifier; E: Naïve bayes; F: K-nearest neighbour; G: eXtreme Gradient Boosting; H: Artificial neural network. ASA: American society of anaesthesiologists; THA: Total hip arthroplasty; DVT: Deep venous thrombosis; AST: Aspartate aminotransferase.

Figure 4 Comprehensive importance of features. ASA: American society of anaesthesiologists; AST: Aspartate aminotransferase.

Figure 5 Receiver operating characteristic curve for machine learning models after cross-validation. ROC: Receiver operating characteristic; 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.

Figure 6 Schematic diagram of an artificial neural network. ASA: American society of anaesthesiologists; eLOS: Extended length of stay; AST: Aspartate aminotransferase.