Enhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach
Keywords:
Dengue fever diagnosis, LSTM, GSCV, RSCV, Rule extraction, Recursive feature eliminationAbstract
Dengue fever poses a critical public health challenge worldwide, particularly in developing countries with laboratory-constrained healthcare systems. Existing methods show promise but are prone to overfitting, which can be mitigated by LSTM-based deep learning models. However, LSTM inherently lack interpretability, a major barrier to clinical adoption in disease prediction. This study bridges robustness and clinical interpretability by integrating rule extraction with LSTM for dengue fever prediction, using a Mendeley dengue fever dataset comprising 1,003 instances and 9 features, preprocessed with label encoding, simple imputation, and synthetic minority oversampling (SMOTE).The LSTM algorithm is optimized using GridSearchCV and RandomizedSearchCV and combined with G-REX for transparent generation, evaluated using accuracy, precision, recall, F1 score, and ROC AUC. All three LSTM variants, including Baseline, GridSearchCV, and RSCV (20 iterations), achieve 100% across Accuracy, Precision, Recall, F1-Score, and ROC-AUC. However, G-REX rules deliver exceptional practical performance: 98.99% Accuracy, 98.53% Precision, 100% Recall, 99.26% F1-Score, and 99.09% ROC-AUC, while maintaining 93.3% fidelity to LSTM predictions, affirming medical validity. These findings position LSTM models as a strong reference standard for dengue classification, enabling resource-limited clinics to achieve high-quality diagnostics through transparent, rule-based support systems. This study can be strengthened in future by exploring other rule-extraction techniques and incorporating diverse, time-series datasets to enhance reliability, interpretability, and generalizability across varied clinical settings.
