This project aims to identify and quantify risk factors for cardiovascular disease (CVD) using a large-scale population health database. The research will follow these key steps:
Data Collection: Access and extract relevant data from a comprehensive health database, which includes demographic, lifestyle, and clinical data as well as cardiovascular outcomes from a diverse population.
Variable Selection: Focus on key variables such as age, gender, smoking status, physical activity levels, dietary patterns, cholesterol and blood pressure levels, diabetes status, and psychosocial factors (e.g., stress, socioeconomic status).
Data Analysis:
Perform descriptive statistics to establish baseline population characteristics.
Conduct multivariable regression analysis to identify and quantify the independent risk factors associated with CVD incidence and outcomes.
Investigate interactions between various factors, such as the combined effects of lifestyle and clinical variables, to highlight complex risk patterns.
Interpretation: Analyze the results to understand the relative importance of each risk factor and any potential synergistic effects between them.
Outcome: The findings will provide detailed insights into the most significant risk factors contributing to CVD. This information can be used to inform public health policies, design targeted prevention strategies, and guide clinical practice in managing CVD risk more effectively.
