Predicting Patient Length of Stay Using Machine Learning Models

Unnecessary days in the hospital may lead to increased hospital-acquired patient complications. So, predicting the length of stay (LOS) for patients is crucial for hospitals to optimize resource efficiency and deliver quality treatment. However, the healthcare industry faces challenges in accessing large-scale training data due to confidentiality concerns. In this paper, we apply seven machine-learning models to ten datasets collected from ten hospitals to investigate. The comparison study aims to investigate the performance of the applied models and produce the fittest model to manipulate the selected dataset and properly solve the length of stay problem. Also, we test the performance of these models using three evaluation metrics. The results show that the eXtreme Gradient Boosting (XGBoost) model outperforms the other machine learning models.