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

Abstract

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.

The research leverages patient data to build predictive models that assess risk levels and monitor disease trends over time. Advanced tools, such as machine learning algorithms and time-series regression, enhance accuracy and provide actionable insights. These methods enable healthcare professionals to allocate resources effectively, design targeted interventions, and reduce the burden of vascular diseases on populations.

The findings emphasize the importance of integrating predictive analytics into vascular disease management strategies, offering a scalable and data-driven framework to improve patient care and public health outcomes.