EnbinYang/tb_prediction_files
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
Implements a complete epidemiological forecasting pipeline with data preprocessing (kNN imputation, LASSO feature screening, random forest ranking) feeding into an ensemble architecture combining ARIMA and multi-step LSTM models. Incorporates SHAP analysis for model interpretability, enabling clinicians to understand which meteorological, economic, and social factors drive TB incidence predictions. Designed for reproducibility with raw Chinese datasets and sequential Python scripts covering imputation through final explanation.
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May 24, 2022
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