Dengue Cases Prediction Using Machine Learning Approach

Abstract

Dengue fever, spread by mosquitoes, affects about 3.9 billion people worldwide. Health officials could use indicators of dengue fever outbreaks to start taking preventative measures. Controlling dengue fever may be more straightforward for local authorities if they have timely and accurate disease forecasts. As one of the most rapidly spreading diseases globally, dengue fever is a threat to everyone. Dengue outbreaks can be predicted using machine learning, according to this study. Dengue prediction models could benefit from nature-based algorithms being boosted or used. The only thing that mattered in the prediction and training model was the week of the year, which was the only thing that signified. A standard machine learning algorithm cannot simulate long-term dependencies in time-series data, which is necessary for accurate projections in Dengue fever cases. When it comes to developing risk criteria for severe Dengue, machine learning could be a valuable implement in determining the possible behavior to formulate.

Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models

“…Many studies have been conducted on dengue prediction, employing various methods, including time series, machine learning, and mathematical approaches. Among the recent studies are Aziz and Aziz (2021), Keshavamurthy et al (2022), Roster et al (2022), and Kaur and Sharma (2023), who applied machine learning, Juraphanthong and Kesorn (2021), Othman et al (2022), and Waseem et al (2023) considered time series approach, and Ewing et al (2021), Vásquez et al (2022), andWang et al (2022) who deployed mathematical modeling.…”