1. Introduction

\label{introduction}
Dengue is a mosquito based viral disease that can be quickly spread in all regions of the world. It can be communicated through female mosquitoes named Aedes aegypti. The main reason behind wide spreadation of dengue disease over tropics due to variations in rainfall, temperature and unplanned rapid urbanization. In recent years, dengue cases have grown up rapidly around the whole world, but the actual numbers of dengue cases are not reported and sometimes also misclassified. According to the WHO report, every year, 390 million dengue infections are reported in the entire world, out of this 96 million are clinically reported with severity of disease [1]. The other study, on the occurrence of dengue disease indicates that dengue viruses can infect 3.9 billion people in 128 countries [2]. The number of cases registered for dengue is increased from 2.2 million (in 2010) to 3.2 million (in 2015). Dengue is one of most fatal and widespread arboviral infection in the globe today. It is an increasingly prevalent tropical arbovirus infection with significant morbidity and fatality rate [3]. Dengue infection has been recognized to be endemic in India for over two centuries as a benign and self-limited disease. In recent years, the disease has shifted its course manifesting in the severe form as DHF and with increasing frequency of outbreaks [4]. Dengue infection in a previously non-immune host produces a principal response of antibodies characterized by a slow and low-titer antibody response. IgM antibody is the first immunoglobulin isotype to appear. In a suspected case of dengue, the presence of antidengue IgM antibody suggests recent infection. Anti-dengue IgM detection using enzyme-linked immunosorbent assay (ELISA) represents one of the most important advances and has become an invaluable instrument for routine dengue diagnosis [5].
In recent years, various decision support system and diagnostic models have been developed for improving experiences and abilities of physicians to accurate detection and diagnosis of diseases. From the literature, it is noticed that artificial neural networks have been widely used in the field of medical data mining and number of decision support systems have been developed with the help of ANN due to its ability of prediction, parallel operation and adaptivity [6-13]. The multilayer neural networks (MLNNs) have been successfully used in replacing conventional pattern recognition methods for the disease diagnosis systems and it can be back- recognized as a powerful tool for training of the MLNNs [6-10]. In this work, a PSO-ANN based diagnostic model is proposed for earlier detection of dengue disease. In the proposed model, PSO method is employed to optimize the parameters of ANN approach. Further, the optimized ANN is applied for the detection of dengue affected patients. The remainder of the paper is organized as follows. Section 2 summarizes the related works in the field of disease diagnosis and detection. Section 3 illustrates dengue disease dataset and its attribute information. In section 4, artificial neural network approach is discussed. The proposed diagnostic model is explained in section 5. The results of study are presented in Section 6. Finally, the entire work is concluded in section 7.