Machine Learning and Deep Learning Models for Dengue Diagnosis Prediction: A Systematic Review

Daniel Cristobal Andrade Girón, William Joel Marín Rodriguez, Flor de María Lioo-Jordan, Jose Luis Ausejo Sánchez

The global crisis triggered by the dengue outbreak has increased mortality and placed significant pressure on healthcare services worldwide. In response to this crisis, there has been a notable increase in research employing machine learning and deep learning algorithms to anticipate diagnosis in patients with suspected dengue. To conduct a comprehensive systematic review, a detailed analysis was carried out to explore and examine the machine learning methodologies applied in diagnosing this disease. An exhaustive search was conducted across numerous scientific databases, including Scopus, IEEE Xplore, PubMed, ACM, ScienceDirect, Wiley, and Sage, encompassing studies up to May 2024. This extensive search yielded a total of 2723 relevant articles. Following a rigorous evaluation, 32 scientific studies were selected for the final review, meeting the established criteria. A comprehensive analysis of these studies revealed the implementation of 48 distinct machine learning and deep learning algorithms, showcasing the heterogeneity of methodological approaches employed in the research domain. The results indicated that, in terms of performance, the support vector machine (SVM) algorithm was the most efficient, being reported in 25% of the analyzed studies. The Random Forest algorithm was the second most frequently used, appearing in 15.62% of the 32 reviewed articles. The PCA-SVM algorithm (poly-5), a variant of SVM, emerged as the best-performing model, achieving 99.52% accuracy, 99.75% sensitivity, and 99.09% specificity. These findings offer significant insights into the potential of machine learning techniques in the early diagnosis of dengue, underscoring the necessity to persist in exploring and refining these methodologies to enhance clinical care in cases of this disease.