[Objective] This paper tries to build an accurate and effective forecasting model for major infectious diseases based on multi-machine learning, aiming to predict outbreak trends and help formulate countermeasures in advance. [Methods] We established an ensemble prediction model with three machine learning optimal weight combinations of ANFIS, LSSVM and LSTM from the Gray Wolf Optimization algorithm. Then, we assessed the model’s prediction performance with the COVID-19 epidemic data. [Results] The ANFIS, LSSVM, and LSTM were suitable for predicting confirmed cases, death cases, and recovery cases. The average R2 of the proposed model reached 0.989, 0.993 and 0.987for the three scenarios. The average RMSE were 37.37%, 63.93% and 53.37% lower than the single model, respectively. [Limitations] The model needs to be examined with data sets on other major infectious diseases. [Conclusions] The ensemble prediction model based on Gray Wolf Optimization can effectively merge the advantages of multiple machine learning models to obtain stable and accurate results.
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