Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery

Abstract

Objective: This study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery. Methods: Patients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data. Results: Three hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance. Conclusions: According to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications.