A time series analysis and comparison of predictive models for the demand for healthcare equipments

Healthcare is a sector where a good planning is in need to optimize the use of healthcare equipments. The demand for hospital beds can vary due to certain reasons such as sudden outbreak of COVID-19 pandemic, spread of seasonal diseases and many more. Therefore, it is very important to focus upon hospital bed management, and it is much dependent on patient length of stay. In this paper, we have done an exploratory data analysis, time series analysis on a publicly available dataset of 32 countries and some predictions about hospital beds, patient length of stay has been made with the help of Machine learning techniques. When the performance of models was compared in terms of R2 score, Random Forest regressor outperformed. The outcome of this study can help healthcare managers to improve hospital services and plan the resources efficiently.