Abstract:
An interruption or reduction in the supply of blood to the cerebrum produces a cerebral stroke.
This supply shortage leads in a deficit of oxygen or vital nutrients, which causes brain cells to die.
Stroke occurs mostly as a result of people's lifestyle choices in the advanced time-changing factors,
for example, excessive glucose levels, heart disease, stoutness, and diabetes. Developing countries
account for 85 percent of all stroke deaths worldwide. The early termination of a cerebral stroke
is critical for effective counteraction and therapy. The best way to deal with this risk is to prevent
it from happening in the first place by managing the relevant metabolic factors. Nonetheless, it is
difficult for clinical workers to determine how much additional safety precautions are necessary
for an expected patient based only on the examination of physiological indicators unless they are
plainly abnormal. Examination reveals that behaviors extricated from various hazard limits
transmit critical information for the prediction of stroke. The data was obtained from the Harvard
Dataverse Repository and was properly prepared and tested using machine learning techniques
such as RF, LR and KNN. We implemented the RF, LR, and KNN algorithms with hyperparameter
tuning in this study and determined the best method among them. For performance evaluation, we
use the AUC-ROC curve, the Precision-Recall curve, and the F1-score, and all reports reveal the
best strategies. This strategy may be seen as a different option, with a low cost and a constant
analytic technique that can get exact stroke prediction.
