Abstract:
Dengue fever, also a viral infection spread by mosquitoes, is still a major global health concern,
impacting millions of people each year. By applying a carefully controlled dataset of 521 entries
and 23 variables, this study analyzes the predictive efficacy of various machine learning methods
for Dengue Fever. Among the methods tested, SVM outperforms the others, obtaining an excellent
accuracy of 98.88%. This remarkable accuracy highlights the algorithm's ability to capture
complex patterns within the multidimensional dataset, establishing it as a strong choice for Dengue
Fever detection. MLPclassifier comes in second with an impressive accuracy of 97.78%, indicating
its ability to handle the dataset's constant characteristics. The success rate of Logistic Regression
and GaussianNBis 96.95% and 93.64%, respectively, illustrating how they adjust to the dataset's
complexities. BernoulliNB, on the other hand, lags behind with a lower accuracy of 67.85%,
indicating limits in dealing with the dataset's peculiarities, particularly given its affinity for binary
features. SVM exceptional accuracy highlights its promise as a significant tool for effective
Dengue Fever detection. The study provides essential knowledge for health professionals and
academics, guiding the selection of the most successful modeling algorithms in the context of
infectious diseases.
