This study investigates the application of machine learning techniques for predicting brain stroke risk, aiming to enhance early diagnosis and intervention strategies. As brain strokes remain a leading cause of morbidity and mortality globally, timely prediction is crucial for effective treatment and improved patient outcomes. We employ a variety of machine learning algorithms, including Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines, to analyze a comprehensive dataset comprising clinical, demographic, and lifestyle factors associated with stroke risk. Through rigorous feature selection and model validation, our analysis identifies key predictors of stroke and evaluates the performance of each algorithm in terms of accuracy, sensitivity, and specificity. The results demonstrate that machine learning models can significantly outperform traditional statistical methods, offering a reliable framework for stroke prediction. This research contributes to the ongoing efforts to integrate advanced analytics into clinical practice, emphasizing the importance of data-driven approaches in enhancing healthcare decision-making and ultimately reducing the incidence of brain strokes.

This study investigates the application of machine learning techniques for predicting brain stroke risk, aiming to enhance early diagnosis and intervention strategies.