Prediction of in-hospital mortality risk in intensive care unit with support vector machine

Abstract

Objective To explore the application of support vector machine (SVM) in predicting the mortality risk after intensive care unit (ICU) admission. Methods A total of 18 094 ICU inpatients from MIMIC Ⅲ dataset were enrolled in the study. The total data set (n=18 094) was randomly divided into training data set (n=12 666, 70%) and test data set (n=5 428, 30%). Based on the Python, the machine learning algorithm, SVM, was used to establish a prediction model of the mortality risk after ICU admission with the results of LASSO feature selection. The efficacy of model was evaluated using the test data set. Results The areas under the receiver operating characteristic (AUCROC) curves of the SVM-based model for predicting the mortality risk in 24 h and 48 h after ICU admission were 0.805 1 (0.793 6~0.816 6) and 0.811 7 (0.799 9~0.824), with sensitivities of 0.751 3 and 0.737 2, and specificities of 0.713 0 and 0.742 9, respectively. Conclusion The SVM-based model for predicting the mortality risk after ICU admission has a satisfactory result and high accuracy.

Keywords

• support vector machine
• artificial intelligence
• prediction mode
• in-hospital mortality risk
• intensive care unit