ICU mortality prediction using modified cost-sensitive PCA and chaos PSO
Jiankang Liu, XianXiang Chen, Zhen Fang, Kai Tong, Lipeng Fang, JunXia Li · 2017
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by MCSPCA, to solve these problems. This modified method not only reduced the feature dimensionality but also better handled the imbalanced problem of the benchmark data. A support vector machine (SVM) model was used as the classifier to identify ICU mortality risk. As for SVM parameters optimization, a chaos particle swarm optimization (CPSO) was used to optimize the penalty parameter and the kernel parameter. The proposed model was compared with several contrast models (such as model without PCA). The test results indicate that the proposed model showed highest AUC of 0.7718 and minimum consumption time of 814s.