Tree-based ensemble models for predicting the ICU transfer of stroke in-patients

The cost for Intensive Care Unit (ICU) resources is extremely high and it affects healthcare budget that provides quality healthcare service for patients. Thus, the need for a predictive model for the decision to transfer stroke in-patients to the ICU is very important in order to utilize the ICU resources effectively. Also, this decision will help to lower morbidity and mortality rates through earlier detection and intervention. Tree-based ensemble approach can be used to improve the accuracy and performance of models. In this research the boosting methods such as Generalized Boosted Model (GBM) and Adaptive Boosting (AdaBoost. M1) were trained and tested on the balanced stroke inpatient dataset. Also, bagging methods such as Random Forest and Bagged AdaBoost (Adabag) were trained and tested on the same dataset. The boosting model, AdaBoost. M1, and bagging model, Random Forest, achieved better accuracy compared to the other models. Therefore, these two models could be used for helping healthcare professionals in decision-making.