Enhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach

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dc.contributor.authorKolawole Muhammed Abdulsalam
dc.contributor.authorHamidat Ayodeji Arikewuyo
dc.contributor.authorIsmail Idowu Akuji
dc.contributor.authorAbdulmumeen Sekinat Shaban
dc.contributor.authorIslamiyat Oluwakemi Giwa
dc.contributor.authorMuhammad Sharafadeen Jimoh
dc.contributor.authorTaofik Abiodun Ahmed
dc.contributor.authorShakirat Ronke Yusuff
dc.contributor.authorRonke Seyi Babatunde
dc.date.accessioned2026-04-27T08:46:17Z
dc.date.available2026-04-27T08:46:17Z
dc.date.issued2026-04-23
dc.description.abstractDengue 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, rulebased 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.
dc.identifier.citation11. Kolawole Muhammed Abdulsalam, Hamidat Ayodeji Arikewuyo, Ismail Idowu Akuji, Abdulmumeen Sekinat Shaban, Islamiyat Oluwakemi Giwa, Muhammad Sharafadeen Jimoh, Taofik Abiodun Ahmed, Shakirat Ronke Yusuff, Ronke Seyi Babatunde (2026). Enhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach. Journal of Institutional Research, Big Data Analytics and Innovation, 2(1). DOI: https://doi.org/10.5281/zenodo.19604856
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6780
dc.language.isoen
dc.publisherco-hosted by Baze University Big Data Analytics and Innovation Research Group, American University Yola, and Sule Lamido University
dc.titleEnhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach
dc.typeArticle
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