Particle Swarm Optimization assisted Support Vector Machine based Diagnostic System for Dengue prediction at the early stage

Nowadays, Surgeons, nurses, radiologists, pharmaceutical specialists, and other health professionals are increasingly relying on artificial intelligence. Dengue fever is the most frequent of the vector-borne viral illnesses. Dengue fever is the leading cause of death in people because the symptoms appear in advanced stages, making it difficult to identify and contributing to a high fatality. As a result, early detection of Dengue is essential for the diagnostic process and improves the chances of effective treatment. This paper proposes a Particle Swarm Optimization aided Support Vector Machine based Diagnostic System for early Dengue prediction. The major goal of this article is to assess the effectiveness of the PSO and SVM for mining the Dengue dataset. The goal of this research is to increase the machine-learning algorithm accuracy. The suggested approach was further validated using a variety of standard Dengue classification data sets. Based on the numerous standard quality of service criteria, a comparison is made between the proposed and current approaches. The results of the experiments show that the suggested approach is more efficient.