Brain Stroke Prediction in Healthcare Using Machine Learning

This paper presents a machine learning approach for predicting the risk of brain stroke in healthcare using the Random Forest algorithm. With the increasing prevalence of stroke cases worldwide, early detection and prevention are crucial. Leveraging a dataset comprising various demographic, clinical, and lifestyle factors, we employ Random Forest to develop a predictive model. The algorithm demonstrates its efficacy in identifying key risk factors associated with strokes, enabling healthcare practitioners to make informed decisions regarding patient care and intervention strategies. Through extensive experimentation and evaluation, our model achieves notable accuracy and robustness, outperforming traditional methods. Furthermore, the interpretability of Random Forest facilitates understanding the underlying mechanisms contributing to stroke susceptibility, aiding in the development of personalized preventive measures. This research contributes to advancing stroke prediction methodologies, fostering proactive healthcare interventions, and ultimately enhancing patient outcomes and quality of life.