A Support Vector Machine predictive model achieved a higher area under the ROC curve (0.929) compared to a Logistic Regression model (0.734) for predicting vomiting after orthopedic surgery PCEA.
Observational (n=195)
Does an SVM-based predictive model improve the prediction of vomiting in orthopedic patients receiving PCEA compared to an LR-based model?
An SVM-based predictive model demonstrated superior performance compared to logistic regression in identifying orthopedic patients at high risk for vomiting after PCEA.
Absolute Event Rate: 0.929% vs 0.734%
Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.
Wu et al. (Wed,) conducted a observational in Postoperative vomiting during patient-controlled epidural analgesia (n=195). Support Vector Machine (SVM) predictive model vs. Logistic Regression (LR) model was evaluated on Area under the ROC curve (AUC) for predicting vomiting. A Support Vector Machine predictive model achieved a higher area under the ROC curve (0.929) compared to a Logistic Regression model (0.734) for predicting vomiting after orthopedic surgery PCEA.
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