Abstract:
Dengue virus is created in the medium of Aedes mosquito. Because the virus is more
prevalent in tropical areas and people are more affected, people in these areas should
be aware of it as a health concern and have effective disease management measures in
place. Reduction and prediction of dengue outbreaks is crucial for prevention and
reduction of mortality. As the death rate has been decreasing in recent years, recently
machine learning algorithm are all of them models have the potential to dengue
prediction and reduce mortality as they analyze correlations between large datasets. All
are algorithms are an too much basis for dengue risk prevention and control. Dengue is
dangerous and sometimes fatal. This fever infection has become a major problem. So
we need to predict the dengue outbreak and reduce mortality. Provides an overview of
various ML methods used in dengue prediction including Logistic Regression models,
DT, KNN. Examining the limitations of each, discussing features, selection, model
interpretability and in addition, we discuss related data for dengue forecasting and
mortality reduction, such as models for different geographic regions. My aim to
decrease the accuracy of dengue forecasting models and provide directions for future
research. Various ML algorithms are used to analyze dengue data sets of summer
patient illness in Dhaka city to reduce dengue prevalence and mortality. Our results
suggest that applying machine learning techniques is an accurate tool to predict dengue.
which is used to reduce the effects of disease on humans, including incorporating real
data. ML plays a role in dengue prediction.
