Exploring Machine Learning Algorithms for the Prediction of Dengue: A Comprehensive Review

Abstract

Vector-borne diseases, transmitted by blood-feeding arthropods like mosquitoes, ticks, and fleas, pose an escalating challenge to global public health. Dengue, a disease propagated by Aedes mosquitoes, is currently the most rapidly spreading vector-borne illness worldwide. Given its endemic nature, the prevention and control of outbreaks remain a global imperative. Timely detection of dengue is critical to mitigate mortality rates, making predictive models indispensable tools for public health planning, resource allocation, and disease control. This study undertakes a comprehensive review of various machine learning algorithms used in developing predictive models for early-stage dengue detection based on presented symptoms. The review encompasses the entire modeling process, including data preprocessing, algorithm implementation, evaluation, and validation. It further delves into the algorithms' ability to accurately classify dengue into febrile, critical, or convalescent phases. An array of machine learning algorithms, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, Artificial Neural Network, and Naive Bayes Classifier were analyzed. The advantages and disadvantages of these algorithms are discussed to identify the most effective approach for dengue prediction. The Naive Bayes algorithm was found to quickly generate predictions with a precision value of 99.1%. However, the SVM model outperformed all others with a cross-validation score of 98.5%, K-Fold validation of 97.5%, precision of 98.2%, and an F1 Score of 98.0%, thereby enhancing the overall performance of the predictive model.