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Article summary:
1. This paper proposes a prediction model of air pollutants, Influenza-like Illness (ILI) and respiratory disease using Long short-term memory (LSTM) and Autoregressive integrated moving average (ARIMA).
2. The paper aims to compare the different datasets of five years and ten years terms, as well as applying feature selection methods for the training model of LSTM.
3. The paper also discusses Root Mean Squared Error (RMSE), Feature Selections, Air Pollutants and Public Health Correlations.
Article analysis:
The article is generally reliable in its presentation of the proposed prediction model of air pollutants, ILI, and respiratory disease using LSTM and ARIMA. The article provides a clear explanation of the objectives of the study, as well as discussing relevant topics such as Root Mean Squared Error (RMSE), Feature Selections, Air Pollutants and Public Health Correlations. However, there are some potential biases that should be noted. For example, the article does not provide any evidence for its claims regarding the correlation between air pollution and diseases such as ILI or respiratory illness. Additionally, it does not explore any counterarguments or present both sides equally when discussing these correlations. Furthermore, there is no mention of possible risks associated with this type of prediction model or how it could be used in practice. In conclusion, while this article is generally reliable in its presentation of the proposed prediction model, more evidence should be provided to support its claims regarding air pollution and disease correlations.