Machine Learning Approaches for Predicting Patient Length of Stay in Hospitals

Abstract

This paper presents a comprehensive study on the application of machine learning techniques to predict the length of stay (LOS) of patients in hospitals. Accurate prediction of LOS is crucial for effective resource planning and management in healthcare facilities. We explore various machine learning approaches, including traditional models and deep learning algorithms, to develop predictive models for LOS. Our study uses a large dataset of patient records, including demographic information, medical history, and clinical features, to train and evaluate the models. We compare the performance of different algorithms and discuss the implications of our findings for healthcare providers. Our results demonstrate the potential of machine learning in improving patient care and hospital operations through better LOS prediction.