Machine Learning as a tool to Predict Cerebral strokes in the patients

A cerebrovascular attack, or CVA, is a condition in which a portion of the brain’s blood supply is cut off, causing the body part that the brain cells control to stop functioning. This is commonly referred to as a stroke. Blood circulation interruptions may result in bleeding (healing within cerebral cells) or ischemia (loss of blood flow). Strokes can result in mortality or lifelong impairment, making them life-threatening. therapy options exist for ischemic strokes, but it is imperative to initiate therapy within the initial few hours following the onset of symptoms. In order to anticipate strokes in patients, machine learning is an ideal approach. The findings suggest that the collective machine-learning models possess more accuracy than standalone machine-learning learning models. As a meta-learner, KW is employed in the suggested ensemble method, and it demonstrated superior performance compared to standalone machine learning classifiers like RF, Light GBM, and Cat Boost