An Analytical Review of Environmental and Machine Learning Approaches in Dengue Prediction

In recent years, dengue has gained prominence as a priority public health challenge due to increasing incidences of spread. The main objective of this systematic literature review (SLR) is to explore the use of environmental factors and machine learning (ML) techniques to combat dengue, based on studies published between 2020 and 2024. For this purpose, 56 studies were selected from a balanced distribution of PubMed, Web of Science, Scopus and Springer Link, under the Preferred Reporting Items for Systematic Reviews and meta-analyses (PRISMA) method. The results obtained made it possible to determine that the climatological variables, such as temperature difference, humidity concentration and rainfall volume, are conditioning factors in the spread of the dengue virus. As for ML models, Random Forest and Support Vector Machines proved to be more accurate than traditional methods in detecting risk areas. The highest scientific production corresponded to the year 2024, with 25% of the studies, while India, with 14.29%, and the United States, with 12.50%, stood out as the countries with the highest contribution. In conclusion, ML techniques have enormous potential for strengthening early detection systems and optimizing resources in high-risk areas, but further research is needed in this field due to the lack of data availability and replicability of models.