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Early Dengue Prediction in Bangladesh: A Comparative Study With Feature Analysis, Explainable Artificial

Md Atik Bhuiyan¹, Md Rashik Shahriar Akash¹, Radiful Islam¹

¹Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh, daffodilvarsity.edu.bd.

Journal of Tropical Medicine

|December 16, 2025

Summary

A custom artificial neural network (ANN) achieved 97.5% accuracy in predicting dengue fever using patient symptoms, outperforming 12 other models. This highlights the potential of machine learning for early dengue detection.

Keywords:

Bangladesh artificial neural network deep learning dengue fever early detection explainable artificial intelligence feature analysis hyperparameter tuning predictive modeling

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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

Area of Science:

Machine Learning
Public Health
Epidemiology

Background:

Dengue fever is a significant public health concern in tropical and subtropical areas.
Early detection of dengue is vital for effective management and control.

Purpose of the Study:

To compare the effectiveness of 13 machine learning and deep learning models for nonclinical, symptom-based dengue prediction.
To identify the most accurate model for dengue prediction in the Bangladeshi population.

Main Methods:

A dataset of 500 patient records with 22 symptom-based features was used.
Evaluated various classifiers including tree-based, linear, and instance-based models.
A custom artificial neural network (ANN) was developed and hyperparameter-tuned.

Main Results:

The hyperparameter-tuned ANN achieved the highest accuracy at 97.5%.
Random forest models showed strong performance with 93.2% accuracy.
SHapley Additive exPlanations (SHAP) identified key predictors like retro-ocular pain, swollen eyelids, and age.

Conclusions:

A well-tuned ANN is superior for symptom-based dengue prediction.
Broad model comparison and explainability are crucial for developing reliable diagnostic tools.
Machine learning offers a promising approach for early dengue detection and public health intervention.