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Hypertension remains a primary global driver of cardiovascular disease, yet traditional linear risk estimators struggle to capture non-linear physiological interactions, while complex machine learning ensembles often operate as opaque "black boxes." This thesis addresses this challenge by developing an interpretable machine learning framework and interactive what-if clinical dashboard su
