An Optimization Precise Model of Stroke Data to Improve Stroke Prediction

Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately precisely detecting possibility aid prevention. We tackle the overlooked aspect of in healthcare literature. predicting general context rather than specific subtypes. clarification will not only ensure clear understanding our study’s scope but also overall transparency impact findings. construct an optimization model describe effective methodology algorithms for machine learning classification, accommodating missing data imbalances. models outperform previous efforts prediction, demonstrating higher sensitivity, specificity, accuracy, precision. Data quality preprocessing play crucial role developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy 97% recall score. In-depth analysis advanced techniques improve prediction. highlights value data-oriented approaches, leading enhanced risk factors. These methods can be applied other medical domains, benefiting patient care outcomes. By incorporating findings, efficiency effectiveness system improved.