Page 70 - FULL REPORT 30012024

Table 2.3 (Continued)

Stroke Disease This project aims Logistic The results from the
Detection and to develop and Regression (LR), study demonstrate
Prediction Using evaluate machine Decision Tree that the random

Robust Learning learning models (DT) forest classification
Approaches for predicting the Classification, method achieves a
likelihood of a Random Forest high accuracy of 96
stroke occurring in (RF) percent, offering
the brain. Classification, potential for early

and Voting stroke detection and
Classifier improved patient
outcomes through
timely treatment.

Comparative To support data- Uses R This project used
Approach of driven decision- programming open-source tools to
Tracking COVID-19 making and joint language. build dashboards to
in Balkan Countries coordination track and monitor
Shiny Web
Using Interactive between health COVID-19
Framework.
Web-Based institutions and indicators at the
Dashboard government bodies national or regional
Provide
in the Balkan level provides a
dashboard for
countries faced simple and
data
with new COVID- affordable strategy
visualization.
19 waves in the that health
region. organisations in

nations similar to
the Balkans may
easily employ.

53