The invention provides a
disease burden prediction and prevention and control decision-making method and
system based on
machine learning, and relates to the technical field of
disease prediction and
public health decision-making, and the method comprises the steps: obtaining epidemiological data of
tuberculosis and related diseases from an authoritative
database, and carrying out the preprocessing of the epidemiological data; respectively training an XGBoost model, an RF (
Radio Frequency) model and a Prophet model; training a
random forest meta-model by adopting a Stacking fusion strategy, and constructing a
hybrid prediction model; calculating RMSE, MAE, MAPE and Rindex evaluation model performance; based on the obtained
hybrid prediction model; based on the variable importance analysis result and the prediction result, the influence of the key independent variable on the
tuberculosis burden dependent variable is quantified, the effect of the independent variable change on the dependent variable is simulated, and a
tuberculosis prevention and control intervention strategy suggestion is generated. By fusing multi-
source data and a
hybrid modeling technology, tuberculosis prediction precision is remarkably improved,
confidence interval quantization and prevention and control strategies are linked for the first time, and data-driven decision support is provided for global
tuberculosis prevention and control.