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.