UTILIZING PREDICTION AND MONITORING TECHNIQUES WITH CORRELATION AND REGRESSION MODELS FOR THE ANALYSIS AND MANAGEMENT OF VASCULAR DISEASES

Abstract

The increasing prevalence of vascular diseases poses significant challenges to global healthcare systems. Effective prediction and monitoring are essential for early diagnosis, personalized treatment, and improved patient outcomes. This study explores the application of prediction and monitoring techniques using correlation and regression models to analyze and manage vascular diseases. Correlation analysis identifies the relationships between key risk factors—such as age, obesity, hypertension, and lifestyle habits—and the onset of vascular conditions. Regression models, including linear, logistic, and multivariate approaches, predict disease progression, treatment efficacy, and mortality rates.