An Optimized Machine Learning-Based Stroke Prediction: Enhancing Precision Medicine and Public Health

Since heart attacks and other cardiovascular diseases continue to be the world's top causes of death, it is imperative that precise prediction be possible to support early intervention and prevention. In this work, we report a novel ensemble model that predicts cardiac strokes with an amazing 0.99% accuracy, which represents a breakthrough in the area. By carefully combining logistic regression, random forest, and gradient boosting, our ensemble model outperforms conventional stroke prediction techniques in terms of predictive performance. The proposed ensemble model is carefully presented in the article, demonstrating how it might improve cardiac stroke prediction accuracy and reliability. Extensive healthcare datasets covering a wide range of patient features and clinical characteristics were used in rigorous testing to guarantee the model's practicality. The outcomes clearly show how our ensemble model outperforms traditional methods, offering an earlier stroke prediction tool that is more accurate and reliable. Furthermore, it sets the stage for more successful worldwide public health campaigns to lower the incidence and consequences of strokes. The optimized machine learning model's integration of logistic regression, random forest, and gradient boosting is a state-of-the-art method that advances the field and advances public health and precision medicine initiatives.