Stroke is a leading cause of disability and mortality worldwide, highlighting the need for improved outcome prediction methods. This study leverages the UK Biobank’s extensive genomic and imaging datasets to develop and validate predictive models for stroke outcomes, including recovery, cognitive decline, and mortality. The primary objective is to integrate genomic information (such as SNPs and GWAS data) with brain MRI metrics (including white matter hyperintensities and infarct volumes) to identify key genetic variants and imaging biomarkers that influence patient prognosis. The methodology involves rigorous data acquisition and preprocessing, ensuring quality control of genomic data, standardized MRI processing, and addressing missing values through appropriate imputation techniques. Advanced statistical and machine learning approaches, including multivariate regression and neural networks, will be employed to select relevant features and build robust predictive models, evaluated using metrics like AUC-ROC and accuracy through cross-validation. By combining these data types, the research aims to uncover novel biomarkers and develop reliable models that can inform personalized medical interventions, ultimately enhancing prognosis and quality of life for stroke patients.
