Abstract
In present time, large numbers of machine learning approaches have been
applied for analysis of medical data. These approaches have also been
proved its importance through accurate and earlier diagnosis of
diseases. The motive of this work is to develop a diagnostic model for
earlier detection and diagnosis of dengue disease. Dengue fever is a
fatal and life threatening disease spreads through female mosquito’s,
called Aedes aegypti. The symptoms of this fever are similar to other
fibers such as viral flu, Chikungunya, Zika fever, etc. But in this
fever, human life can threaten due to severe depletion of platelets. So,
earlier detection of dengue disease can protect several human lives and
also take curative action before it becomes an infectious disease.
Hence, in this work, an effort is made to develop a PSO-ANN based
diagnostic model for earlier detection of dengue fever. In the proposed
model, PSO technique is applied to optimize the weight and bias
parameters of ANN method. Further, PSO optimized ANN approach is used to
detect dengue affected patients. The effectiveness of the proposed model
is evaluated using accuracy, sensitivity, specificity, error rate and
AUC parameters. The results of the proposed model are compared with some
other approaches like ANN, DT, NB, and PSO. It is seen that the proposed
diagnostic model is an efficient and effective model for more accurate
and earlier detection of dengue fever.
Keywords —Classification, Neural Network, Decision Tree, Naive
Bayes, CART, PSO