Abstract:
Dengue fever continues to pose a major health problem in Bangladesh in crowded city
areas like Dhaka. Here, factors related to the environment and population help the disease
spread. This study looks at how different age groups are prone to dengue infection. It pays
special attention to groups at high risk such as kids (under 18) and older adults (over 60).
These groups face more danger because their immune systems work and they may have
other health issues. The research uses a set of 1,000 unnamed medical records. It looks at
key things like NS1 antigen test results, IgG/IgM antibody levels, and personal details to
find risk patterns in various age groups. The study uses several methods to sort dengue
risk into three age groups: children, adults, and older adults. These methods include
Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Extra Trees Classifier, and
Linear Discriminant Analysis (LDA). These tools figure out complex links in the data and
predict how likely each group is to get dengue. Early findings show that age-specific
immune markers and environmental factors play a big role in determining dengue risk. To
deal with problems like uneven data sets and choosing the right features, the study used
strong data cleaning methods. These included Min Max scaling and looking at how
different factors relate to each other. Future research will zero in on making the dataset
bigger to boost its usefulness across the board tweaking the Machine Learning setup, and
adding more health and economic factors. The end game is to shape public health plans for
spotting and stepping in for people at high risk. This study adds to what we know about
using data to tackle dengue offering ways to scale up solutions for other warm regions
facing the same issues.
