Adaptive logistic group Lasso method for predicting the no-reflow among the multiple types of high-dimensional variables with missing data
Xianglin Yang, Yunhai Tong, Xiangfeng Meng, Shuai Zhao, Zhi Feng Xu, Yanjun Li, Xin Jia, Shaohua Tan · 2016
The prediction of no-reflow phenomenon aroused much attention, because of its independent association with increased in-hospital mortality, malignant arrhythmias, and cardiac failure. Many studies on prediction of no-reflow were carried out focusing on only few predictors. As big data era has been coming, high-dimensional predictors are available for prediction. However, as a common problem, big data analytics in healthcare from the electronic medical record (EMR) system is faced with many challenges, such as missing data processing, multiple types of variables processing and the high-dimensional data prediction. A general method based on improved weighted K-nearest neighbors and adaptive logistic group Lasso was proposed for predicting the no-reflow after cardiac surgery among the multiple types of variables with missing data. Compared with logistic regression, Lasso method, and artificial neural network method, our method has lower misclassification error rate and less complex model for no-reflow prediction, especially when predicting among multiple types of variables with missing data.