| dc.description.abstract | Dengue is a mosquito-borne viral
disease that has dramatically increased around
the world in recent years. The spread of Dengue
depends on the tropics, rainfall, temperature,
relative humidity and unplanned urbanization.
Severe Dengue can lead to circulatory system
failure, shock and even death. The development of
an effective Dengue fever prediction model is
therefore essential for better Dengue case
management. Feature selection is the
predominant phase in developing the Dengue
diagnosis prediction model. It is required to
identify the most crucial attributes, as not all
attributes have notable effects on the results.
Therefore, this study focuses on the feature
selection methods such as Principal Component
Analysis (PCA) and Wrapper feature selections
method with Naïve Bayes, K-Nearest Neighbor
(KNN), and J48 algorithms. Simple Artificial
Neural Networks (ANN) were developed to
validate the performance based on the accuracy of
each feature selection method, since it can work
well with the partial dataset. Myalgia and Retro-
Ocular Pain are the most expressive features
chosen by all wrapper feature selection methods.
In addition, with PCA, the initial 22-dimensional
system was reduced to an 8-dimensional system
with a cumulative variance of 59%. ANN with PCA
resulted in the higher accuracy of 72.47% and
ANN with Wrapper feature selection (KNN)
showed the lowest accuracy of 54.47%. In
conclusion, PCA is identified as the best feature
selection method for the given dataset in this
study based on the accuracy of ANN. In future,
multiple Dengue diagnosis prediction models can
be developed with higher accuracy and efficiency
using the most vital attributes. | en_US |
