Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals.