ALGORITHMIC MODEL FOR ANTICIPATING BRAIN STROKE

Brain stroke diagnosis, when done correctly and early, is a major medical challenge that can have a major effect on patient outcomes. Optimizing machine learning methods presents a viable way to improve the accuracy and speed of diagnosis. The goal of this research is to create a reliable machine learning model that can forecast the risk of a brain stroke from patient data, such as demographics, medical histories, and outcomes of clinical tests. To achieve high accuracy and generalizability, the model combines ensemble techniques, feature selection strategies, and supervised learning algorithms. Thousands of patient records make up our dataset, which has been carefully pre-processed to account for outliers, missing values, and unequal class distributions. We use metrics like these to assess the effectiveness of different models, such as logistic regression, decision trees, random forests, support vector machines, and F1-score, and the area under the ROC curve. The results demonstrate that our optimized model achieves a significant improvement over traditional diagnostic methods, providing clinicians with a valuable tool for early intervention and personalized treatment planning. This study underscores the potential of machine learning in transforming healthcare diagnostics and underscores the importance of interdisciplinary collaboration in developing such technologies.