HOSPITAL LENGTH OF STAY PREDICTION WITH ENSEMBLE LEARNING METHODE

Abstract

The hospital length of stay (LoS) is the number of days an inpatient will stay in the hospital. LoS is used as a measure of hospital performance so they can improve the quality of service to patients better. However, making an accurate estimate of LoS can be difficult due to the many factors that influence it. The research conducted aims to predict LoS for treated patients (ICU and non-ICU) with cases of brain vessel injuries by using the ensemble learning method. The Random Forest algorithm is one of the ensembles learning methods used to predict LoS in this study. The dataset used in this study is primary data at PHC Surabaya Hospital. From the results of the simulations performed, the random forest algorithm is able to predict LoS in a dataset of treated patients (ICU and non-ICU) with cases of brain vessel injuries. And the simulation results show a type II error value of 0.10 while the value of type I error is 0.16.