Artificial Intelligence-Based Early Detection of Dengue Using CBC Data

Dengue fever is a tropical mosquito-transmitted disease spread through the Aedes mosquito, where the human body works as the primary host. Each year, densely populated countries such as Bangladesh, Thailand, and India, particularly in the Southeast Asian region, experience the majority of dengue outbreaks worldwide. Notably, in 2023, Bangladesh endured an unprecedented dengue outbreak, registering the highest number of cases in over two decades since 2000. This research aims to facilitate early detection of dengue from patients’ complete blood count (CBC) medical laboratory reports collected from two hospitals in Dhaka, Bangladesh. The custom-built dataset, comprising 320 samples and 14 hematology features, is used to evaluate diverse artificial intelligence techniques. This dataset documents suspected dengue cases in Bangladesh from May 2023 to October 2023, reflecting a significant outbreak period, including a gender distribution ratio of 5:3 male to female patients. Various preprocessing steps, handling missing values and outliers, one-hot encoding, synthetic oversampling, and removing redundant features, are applied to the employed dataset. Five feature selection methods and diverse machine learning algorithms, along with ensemble learning and transformer-based models, are implemented. The stacking ensemble classifier achieved the highest performance, with an accuracy of 96.88% and an F1 score of 0.9646. The stacking technique has been built using the LightGBM meta-classifier and XGBoost, Logistic Regression, and Multilayer Perceptron base learners. The collected CBC dengue dataset and the implementation codes are available at:
https://github.com/mritunjoychk17/Dengue-Prediction-in-Bangladesh-Using-Machine-Learning
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