BRAIN STROKE PREDICTION USING MACHINE LEARNING

BRAIN STROKE PREDICTION USING MACHINE LEARNING

 MANUSHA M J 1, ROOPA R 2

 [1]Student Department of MCA, BIET, Davanagere.

[2]Assistant professor, Department of MCA, BIET, Davangere.

ABSTRACT

In our study, we aim to predict the likelihood of a stroke using machine learning. We used a dataset with various patient information, such as age, lifestyle, and medical history. After cleaning and preparing the data, we identified key factors that influence stroke risk. We tested several machine learning methods, including Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks. We found that the Random Forest method was the most accurate, correctly predicting strokes 89% of the time. Our analysis showed that the most important predictors of stroke are hypertension, age, and heart disease. This research demonstrates that machine learning can effectively predict strokes, potentially allowing for earlier and more targeted preventive care in medical environments.

Keywords: artificial intelligence; brain stroke prediction; deep learning