Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

Figure 1 Flow chart of the patient selection and data process. ANN: Artificial neural network; CIC: Clinical impact curve; DCA: Decision curve analysis; DT: Decision tree; RFC: Random forest classifier; ROC: Receiver operating characteristic curve; SVM: Support vector machine; XGboost: Extreme gradient boosting.

Figure 2 Variable screening and weight allocation. A: Variable screening; B: weight allocation. ANN: Artificial neural network; BMI: Body mass index; DT: Decision tree; NACT: Neoadjuvant chemotherapy; RFC: Random forest classifier; SVM: Support vector machine; XGboost: Extreme gradient boosting.

Figure 3 Predictive model visualization based on machine learning-based algorithm. A: The random forest classifier algorithm represents a computational method for effectively navigating the free parameter space to obtain a robust model; B: At the branch of decision tree, age and catheter functioned as the irreplaceable weight in addition to clinical factor indicators. BMI: Body mass index; NACT: Neoadjuvant chemotherapy.

Figure 4 Predictive model visualization based on artificial neural network algorithm. BMI: Body mass index.

Figure 5 Prediction performance of candidate models based on machine learning-based algorithm. ANN: Artificial neural network; DT: Decision tree; RFC: Random forest classifier; SVM: Support vector machine; XGboost: Extreme gradient boosting.