Enhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach
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Date
2026-04-23
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co-hosted by Baze University Big Data Analytics and Innovation Research Group, American University Yola, and Sule Lamido University
Abstract
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, 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.
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Citation
11. 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