No abstract

In recent decades, cardiovascular disease (CVD) has become the leading cause of death in most countries of the world. Since many types of CVD are preventable by modifying lifestyle behaviors, the objective of this paper is to develop an effective personalized lifestyle recommendation algorithm for reducing the risk of common types of CVD. However, in practice, the underlying relationships between the risk factors (e.g., lifestyles, blood pressure, etc.) and disease onset is highly complex. It is also challenging to identify the effective modification recommendations for different individuals due to the uncertainties in individuals' preference to change and disease progression. Therefore, to address these challenges, this study developed a novel data-driven approach for personalized lifestyle behaviors recommendation based on machine learning and a personalized exponential utility function model. The contributions of this work can be summarized into three aspects: (1) a classification-based prediction model is implemented to accurately predict the CVD risk based on the condition of risk factors; (2) the generative adversarial network (GAN) is incorporated to learn the underlying relationship between risk factors, as well as quantifying the uncertainty of disease progression under lifestyle modifications; and (3) a novel personalized exponential utility function model is proposed to evaluate the modifications' utilities with respect to CVD risk reduction, individual's preference to change and disease progression uncertainty, and identify the optimal modification for each individual. The effectiveness of the proposed method is validated through an open-access CVD dataset. The results demonstrate that the personalized lifestyle modification recommended by the proposed methodology can significantly reduce the potential CVD risk. Thus, it is very promising to be further applied to real-world cases for CVD prevention.