Cardiovascular and cerebrovascular diseases are major global health burdens, projected to account for 40% of all deaths by 2030. While research has focused on individual risk factors, such as lifestyle, environmental exposures, and biomarkers, their combined effects on disease progression are not well understood. This study aims to explore how lifestyle, air pollution, sleep quality, dietary habits, blood biomarkers, and imaging characteristics interact to influence disease progression and prognosis.
The research questions include how lifestyle factors, air pollution, sleep quality, and dietary habits collectively impact disease progression, the role of blood biomarkers in prognosis, and how machine learning and radiomics can integrate these factors to improve risk prediction. The objectives are to assess the individual and combined effects of these factors, identify key biomarkers, and develop machine learning models that incorporate epidemiological, clinical, and imaging data to predict disease outcomes.
This research will utilize advanced techniques to integrate diverse data sources and uncover interactions between these factors, aiming to improve our understanding of disease mechanisms and support the development of personalized prevention and treatment strategies, ultimately enhancing patient outcomes.
