Optimizing Stroke Risk Forecasting using Stochastic Boosting and Grid-Adaptive Learning

Stroke is still one of the most common causes of death and long-term disability in the world, and there is no single treatment that works for everyone. Deep learning algorithms are better at predicting stroke risk, but they need large, well-annotated datasets, which are typically hard to find in clinical settings. This study introduces an innovative hybrid predictive framework called Stochastic Boosting Filters with Adaptive Grid-Based Projections (SBF-AGBP) to tackle these challenges and improve the precision and clarity of stroke risk evaluation. The proposed model integrates the best features of stochastic boosting with adaptive grid-based forecasts. Stochastic boosting trains models on different subsets of bootstrapped data again, which makes them more general. Adaptive grid-based projections, on the other hand, divide the feature space into grids that change over time. This makes it easy to understand and use the information. SBF-AGBP also uses heuristics that are specific to the industry to rank important stroke risk factors like cholesterol levels, blood pressure, age, and lifestyle choices. The adaptive grid approach can handle complicated nonlinear interactions between variables while still being rapid to process. Also, the stochastic boosting filters slowly make lousy learners better and lessen the effects of class imbalance. This is a prevalent concern with medical databases. When tested with the IEEE stroke dataset, SBF-AGBP was more accurate, precise, and recall than wellknown machine learning models like Random Forest, XGBoost, and Support Vector Machines. The paradigm also provides clear information about the importance of features, which could make it more useful for making therapeutic decisions. SBFAGBP is a good and simple technique to figure out how likely are to have a stroke soon. It helps people get help quickly and minimizes the number of stroke-related health problems around the world.