Medical meteorological forecast for ischemic stroke: random forest regression vs long short-term memory model.

Abstract

Ischemic stroke (IS) is one of the top risk factors for death and disability. Meteorological conditions have an effect on IS attack. In this study, we try to develop models of medical meteorological forecast for IS attack based on machine learning and deep learning algorithms. The medical meteorological forecast would be beneficial to public health in IS events prevention and treatment. We collected data on IS attacks and climatology in each day from 18th September 2016 to 31th December 2020 in Haikou. Data on IS attacks were from the number of hospital admissions due to IS attack among general population. The random forest (RF) regression and long short-term memory (LSTM) algorithms were respectively used to develop the predictive model based on meteorological data. Performance of the model was assessed by mean squared error (MSE) and root mean squared error (RMSE). A total of 42849 IS attacks was included in this study. IS attacks were significantly decreased in winter. The pattern of climatological data was observed the regularity in seasons. For the performance of RF regression model, the MSE is 243, and the RMSE is 15.6. For LSTM model, the MSE is 36, and the RMSE is 6. In conclusion, LSTM model is more accurate than RF regression model to predict IS attacks in general population based on meteorological data. LSTM model showed acceptable accuracy for the prediction and could be used as medical meteorological forecast to predict IS attack among population according to local climate.