Prediction of Patients’ Length of Stay at Hospital During COVID-19 Pandemic
Jianing Pei, Xin Lin, Qixuan Chen · Journal of Physics Conference Series · 2021
Abstract Machine learning has been extensively used in diverse healthcare settings since the 21st century. Statistical models are proven to be powerful in detecting early disease symptoms and could potentially aid decision-making in the healthcare system. To help improve medical resource allocation during COVID-19 pandemic, we aim to develop machine learning models that predict each patient’s length of stay (LOS) in hospital. Three machine learning models, namely, K-nearest Neighbors Algorithm, Logistic Regression and Random Forest are implemented and optimized on the same healthcare dataset. The final accuracy of each model is 0.3442, 0.3524 and 0.3541 respectively, which are not very high. Our subsequent correlation analysis on the healthcare dataset shows the patients’ features used do not provide sufficient information for accurate LOS prediction. Yet, machine learning approaches could potentially yield much better results if the data quality can be improved by including additional relevant patient features and breaking LOS into more appropriate intervals. More detailed healthcare data should be obtained to make the LOS prediction useful for healthcare management.