Machine Learning Based Early Detection for Brain Stroke

Brain attack or stroke is one of the major causes of illness and death on a global level; it is important to detect it at an early stage to deal with it on time and save lives. Our study shows how machine learning can be used in the prediction of brain strokes by using a dataset of some common clinical features. Our model predicts stroke with approximately 80% accuracy by using traditional logistics regression. Analysis of features shows that age, gender, different diseases, and smoking status are significant in the prediction of brain stroke. Our outcomes reveal how machine learning is helping in medicine to find patients with high risk and comfort early interventions. This approach can assist clinicians in making better decisions and improving patient treatment.