Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution

Abstract

This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi District of China, and the study included 1932 cases. Six machine learning methods were used to forecast the demand for hemorrhagic stroke healthcare services considering seasonality and a lag effect, and the average area under the curve was as high as 0.7971. Our results indicate that (1) the performance of forecasting during the warm season is significantly better than that in the cold season, (2) considering air pollution would improve the performance of forecasting the demand for hemorrhagic stroke healthcare services using machine learning, (3) the association between the demand for hemorrhagic stroke healthcare services and air pollutants is linear to some extent, and (4) it is feasible to use short-term concentrations of air pollutants to forecast the demand for hemorrhagic stroke healthcare services. This practical forecast model could provide an advance warning regarding the potentially high numbers of hemorrhagic stroke admissions to medical institutions, thus allowing time to implement an appropriate response to the increase in patient volumes.