HemaStack: Harnessing Heterogeneous Stacking with Neural Meta-Learning and Dual Explainability for Cost-Effective Dengue Diagnosis

Dengue fever in Bangladesh is a critical public
health issue, particularly in the Dhaka area, with over 30,000
cases reported in 2025, highlighting the need for quick and
reliable diagnostic tools. While routine Complete Blood Count
(CBC) parameters are easily available, traditional diagnosis relies
on NS1 antigen testing, which is expensive and susceptible to false
negatives in later stages of infection. To overcome this limitation,
we propose HemaStack, a stacking ensemble framework for
binary dengue classification using routine hematological blood
parameters. A dataset of 1,037 patients from various tertiary
hospitals in Dhaka, Bangladesh, from June to August 2025, was
preprocessed using label encoding, IQR-based outlier removal,
StandardScaler normalization, and SMOTE balancing. HemaS
tack integrates five base learners—Random Forest, Extra Trees,
XGBoost, LightGBM, and SVM—with an MLP meta-learner
using passthrough feature augmentation, benchmarked against
eight baseline classifiers under 10-fold cross-validation and an
independent test set. HemaStack attained an accuracy of 98.59
± 0.98%, an F1-score of 98.59 ± 0.97%, and a ROC-AUC of
0.9966 ± 0.0037; on the held-out test set it achieved an accuracy
of 97.57%, a recall of 99.38%, an F1-score of 98.45%, a ROC
AUC of0.9852, and an MCCof0.9292, outperforming all baseline
models. SHAP and LIME analyses identified Lymphocytes (%),
Platelet Count, and Hematocrit as the most important features.
The results indicate that HemaStack enables detection of dengue
in a timely, cost-effective, and interpretable manner without
specialized diagnostic tests, which could be useful in the resource
limited healthcare system in Bangladesh and other endemic areas.