Development and validation of the support vector machine model for predicting the risk of death in patients after abdominal surgery

ObjectiveTo develop a model for predicting the 28-day death risk in patients with abdominal surgery using support vector machine algorithm.

MethodsThe preoperative general conditions, preoperative visits, laboratory tests and other indicators of patients treated with abdominal surgery from July 2015 to June 2017 were collected.The logistic regression model was compared to evaluate the performance of support vector machine model.

ResultsA total of 1 512 surgical patients were included, including 911 males(60.25%) and 601 females(39.75%).In both of the training set and validation set, the predicted probability of death in death group was significantly higher than that in survival group(P < 0.01).In the training set, the area under ROC curve of support vector machine model was larger compared with the logistic regression model, but the difference of which was not statistically significant(0.97 vs 0.95, P>0.05).In the validation set, the area under the ROC curve of support vector machine was significantly higher than that of logistic regression model(0.98 vs 0.91, P < 0.05).The sensitivity(training set 68.57% vs 62.86%, validation set 79.78% vs 77.78%) and positive predictive value(training set 80.00% vs 65.75%, validation set 83.33% vs 77.13%) of support vector machine model were better than those of traditional logistic regression model.

ConclusionsThe support vector machine model can accurately predict the risk of 28-day death in patients with abdominal surgery, and its performance is better than that of traditional logistic regression model.