PSOA-LSTM: a hybrid attention-based LSTM model optimized by particle swarm optimization for accurate lung cancer incidence forecasting in China (1990-2021).

BACKGROUND

Accurate forecasting of lung cancer incidence is crucial for early prevention, effective medical resource allocation, and evidence-based policymaking.

OBJECTIVE

This study proposes a novel deep learning framework-PSOA-LSTM-that integrates Particle Swarm Optimization (PSO) with an attention-based Long Short-Term Memory (LSTM) network to enhance the precision of lung cancer incidence prediction.

METHODS

Using the Global Burden of Disease 2019 (GBD 2019) dataset, the model predicts age- and gender-specific lung cancer incidence trends for the next 5 years. The proposed model was compared against traditional models including ARIMA, standard LSTM, Support Vector Regression (SVR), and Random Forest (RF).

RESULTS

The PSOA-LSTM model achieved superior performance across five key evaluation metrics: mean squared error (MSE) = 0.023, coefficient of determination ( ) = 0.97, mean absolute error (MAE) = 0.152, normalized root mean squared error (NRMSE) = 0.025, and mean absolute percentage error (MAPE) = 0.38%. Visualization results across 12 age groups and both genders further validated the model's ability to capture temporal trends and reduce prediction error, demonstrating enhanced generalization and robustness.

CONCLUSION

The proposed PSOA-LSTM model outperforms benchmark models in predicting lung cancer incidence across demographic segments, offering a reliable decision-support tool for public health surveillance, early warning systems, and health policy formulation.