Random forest algorithm for predicting postoperative hypotension in oral cancer resection and free flap reconstruction surgery

Figures

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  Fig. 1. Study design. AICU anesthesia intensive care unit.

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  Fig. 2. Increased burden in patients with oral cancer with postoperative hypotension (POH). (A,B), Patients with POH (vs. non-POH) have significantly higher healthcare expenditure (A) and prolonged hospitalization (B).

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  Fig. 3. Variable shrinkage and selection using the Boruta algorithm. (A), Boruta feature selection model identified 10 of the 50 variables that demonstrated a strong association with POH. The horizontal axis displays the number of variables, while the vertical axis shows the root mean square error value of each variable. (B), Classifier run of Boruta analysis. Every curve represents the Z-value of each variable in the model calculation.

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  Fig. 4. Performance of ML models. (A), ROC curves of the seven ML models, the random forest (RF) outperformed all the six other ML models. (B), Confusion matrix of RF. ML, machine learning; ROC: Receiver operating characteristic; KNN: K-nearest neighbours; RF: Random forest; DT: Decision Trees; LR: Logistic regression; SVM: Support vector machine; XGBoost: Extreme gradient boosting; LightGBM: Light Gradient Boosting Machine.

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  Fig. 5. Feature importance evaluation based on the RF. (A) Summary plots of SHAP values based on the RF. The size of the dots shows the magnitude of the feature’s impact, with larger dots denoting a stronger impact. (B) Feature importance plot based on the RF in terms of accuracy and Gini analysis. A higher value on the X-axis indicates greater importance of the feature in the corresponding model. SHAP: Shapley additive explanation; RF: Random forest.

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  Fig. 6. Constructed SHAP analysis based on the RF for specific individual. (A, B) High-risk (A) and low-risk examples (B) of postoperative low blood pressure in oral cancer resection and free flap reconstruction surgical patients, respectively. The value of f(x) represents the predicted risk of POH for a given sample.