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- W3194142110 abstract "Due to climate change, there have been many studies on the relationship between environmental factors and diseases in recent years. Due to the characteristics of multivariable, nonlinear, sequential, continuous, and delayed effects of meteorological data, we used the Long Short-Term Memory (LSTM) model of machine learning and the dataset from National Health Insurance Research Database (NHIRD) to establish a model predicting the trend of cardiovascular disease (CVD) incidence from 2009 to 2013. The best mean absolute percentage error (MAPE) was 10.98%. This predictive model helps medical institutions make appropriate medical decisions about human resource management and material scheduling, benefit clinical health education, disease prevention, as well as provide suggestions for epidemiology, environmental health, and national health policies." @default.
- W3194142110 created "2021-08-30" @default.
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- W3194142110 date "2021-05-28" @default.
- W3194142110 modified "2023-10-11" @default.
- W3194142110 title "Using Machine Learning to Analyze and Predict the Relations Between Cardiovascular Disease Incidence, Extreme Temperature and Air Pollution" @default.
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- W3194142110 doi "https://doi.org/10.1109/ecbios51820.2021.9510479" @default.
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