Brain Stroke Detection System using Machine Learning

The purpose of this research is to develop an early detection system for brain strokes using machine learning. Strokes require immediate medical intervention to minimize damage, making early detection crucial. This paper reviews existing models and proposes a machine learning-based model capable of analyzing patient data and predicting stroke risks with high accuracy. The methodology includes data preprocessing, feature selection, model training, and validation. Our results indicate a significant improvement in early detection rates, suggesting that machine learning models could assist in early intervention and reduce stroke-related fatalities.