An influenza prediction system, storage medium and device for optimizing lstm and lightgbm parameters

Abstract

The invention discloses an influenza prediction system for optimizing LSTM and LightGBM parameters. The system includes an LSTM module, a LightGBM module, and a prediction module; The predicted value n of the number of influenza infections; the LightGBM is configured to use LightGBM to calculate the probability of influenza infection for each person in the aggregated population based on the health status data and surrounding environment data, and the infection probability sequence is obtained by sorting from large to small; the prediction module is It is configured to select the top n individuals with the highest probability of being infected with influenza as a high-risk group. This invention combines the influenza outbreak trend of the aggregated population with the influenza infection probability of each individual in the aggregated group to accurately predict the susceptible persons in the aggregated population. The LSTM model and the LightGBM algorithm were adjusted to further improve the accuracy of the prediction results.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence, data statistics, medical informatization, etc., and relates to a multivariable LSTM and LightGBM parameter adjustment system, storage medium and device for predicting influenza. Background technique [0002] At present, some progress has been made in the prediction of influenza trends. For example, the prediction of influenza outbreak trends mainly uses linear regression models, time series models, etc., and these prediction methods use historical influenza population data to train the models. The external characteristics such as environmental factors and weather factors that have a certain degree of influence on the percentage of influenza cases, but it is only a prediction of the outbreak trend and cannot accurately identify susceptible persons in a certain range of people. [0003] In the prior art, a technical solution for disease prediction through Long Short Term Memory networks (LST...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the LSTM model is large and the calculation speed is slow and difficult to converge when processing data with many dimensions, which leads to the inability to take into account many types of factors, and the problem that LightGBM is easy to overfit and cause inaccurate calculations, the present invention provides the following technical solutions:

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