Abstract:
The purpose of this study is to predict the course of dengue fever in the Bangladeshi city of
Dhaka by using a classification approach that makes use of diagnostic and demographic
information. The first machine learning models, Support Vector Machine (SVM) and
kNearest Neighbors (KNN), were applied using data samples that included demographic and
medical variables. The results showed predicted accuracy levels of 61% and 66%, respectively (referenced dataset). Later advances included standardizing data scaling and
tuning hyperparameters. These yielded significant gains in performance; SVM reached an
astounding 93%, while KNN exceeded expectations at almost 100%. The study also presented a Meta-hybrid model that uses a stacking-classifier in a
machinelearning ensemble. This novel method used the advantages of separate models to
produce a remarkable 92% prediction accuracy for the dengue outcome of individuals in
particular conditions. The results emphasize
