Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU

Figures & data

Table 1. Statistics result of the causes of craniotomy

Type of craniotomy	Number of patients	Proportion of patients
Decompressive Craniectomy	267	31.94%
Hematoma evacuation	210	25.12%
Resection of meningioma	141	16.87%
Aneurysm intervention	214	25.60%
Resection of pituitary adenoma	48	5.74%

Figure 1. ROC curves for LR, RF, SVM, ANN, and XGBoost models in predicting the mortality of patients with craniotomy.

Table 2. Mortality prediction performance ML models on test sets

Model	AUC	Accuracy	Precision	Recall	F1	Brier
LR	0.78	0.92	0.62	0.16	0.25	0.06
RF	0.80	0.93	0.78	0.21	0.31	0.06
SVM	0.78	0.93	0.63	0.20	0.31	0.19
ANN	0.78	0.92	0.49	0.23	0.29	0.06
XGBoost	0.84	0.93	0.77	0.22	0.34	0.06

Figure 2. Feature importance of XGBoost model sorted by F score to show the features had a greater impact on the outcome.

Figure 3. LIME results of XGBoost model.