In-hospital Mortality Prediction for ICU Patients on Large Healthcare MIMIC Datasets Using Class Imbalance Learning

The problem of class imbalance in the in-hospital mortality prediction for ICU patients is presented. We propose to build a novel predicting model using the balanced random forest (BRF) algorithm, and tune the hyper parameters using a better performance measure, i.e., adjusted geometric-mean. The performance of the model is evaluated using the data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. Our results show that the recall rate of the death class of ICU patients was significantly improved compared with the benchmarking model.