Improving Prediction of Dengue Outbreaks Using Attention-based LSTM Model with Honey Badger Optimization for Hyperparameter Tuning

Climate Change Poses a Significant Challenge to the Current Dynamics of Disease Outbreaks. This Study Improves Outbreak Prediction Using an Attention-Based LSTM Model Optimized by the Honey Badger Algorithm (HBA) for Hyperparameter Tuning.

Using Disease, Climate, and Geographic Data From 2015–2024 in Different Barangays in Koronadal, South Cotabato, the Model Predicts Incidence Over 1-, 3-, 6-, and 12-Month Horizons. Attention Mechanisms Enhanced Long-Term Pattern Detection, While HBA Reduces Overfitting and Boosts Accuracy. Results Show the HBA-LSTM Reduces Mean Squared Error by 43.7% Over Standard LSTM and 22.2% Over Attention Models. Similar Reductions are Seen in RMSE, MAE, and MAPE. Though Effective, Further Tuning and Alternative Architectures are Suggested for Improved Generalization.

LSTM, overfitting, attention mechanism, deep learning, honey badge, optimization, fine-tuning

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