dc.description.abstract |
Stroke, a deadly disease affecting the brain, has damaging outcomes which may
result to death. Its burden has significantly increased in developing countries due
to the lack of resources focusing on stroke healthcare and prevention. The need
to minimize its effects surged the need to be cautious against the disease and use
digital instruments to improve identifying stroke risk. This study implemented
different machine learning techniques to predict the probable occurrence of stroke.
After removing noise and outliers, data pre-processing was applied along with
KNN Imputation to impute missing values. SMOTE was used to handle the
imbalance present in the data and after conducting feature selection with the
use of ExtraTreesClassifier, XGBoost generated the highest performance metrics
among the 7 classifiers. The model was then integrated to the web application
making it possible for users to predict whether or not they have the likelihood of
having the disease. |
en_US |