Machine Learning Techniques for Brain Stroke Analysis and Prediction

Brain strokes a major contributor to global mortality and morbidity requires prompt prediction and intervention to reduce its impact. The study explores the practice of machine learning techniques for predicting brain strokes with the goal of improving early detection and preventive strategies. Utilizing a comprehensive dataset that includes demographic, lifestyle, and medical factors this study employs various machine learning systems such as logistic regression, random forest, decision tree, K-nearest neighbors, AdaBoost, XGBoost, and voting classifiers to develop predictive models. Feature ranking techniques are applied to detect the most significant variables which causes the incidence of stroke. The analysis incorporates preprocessing steps including data normalization, cleaning and managing missing values to determine the quality of the input data. Performance metrics including F1-score, recall, accuracy and precision are applied to measure the proficiency of the established models. Decision tree, XGBoost and voting classifier are showing best performance in this study with a similar accuracy of 96%. Additionally, this study contributes to build up a stacking model which achieved higher performance with an accuracy of 97%.