Cardiovascular Disease Management Via Rule-based Personalized Lifestyle Recommendation



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Cardiovascular disease (CVD) is a major cause of death worldwide, and its onset is highly correlated with various predictors such as age, gender, and lifestyle. Several types of CVD are preventable by modifying lifestyle behaviors, but the existing guidelines on lifestyle modification were developed for the general population and have limited utility on individuals. Numerous machine learning models were developed for personalized lifestyle recommendation by predicting individual’s CVD risk from associated predictors and searching for the modifications on lifestyle predictors that maximally reduce the CVD risk. However, most machine learning models function as a black box where models predict and manage CVD without knowing the contribution of each predictor and how to interpret the causes of CVD. Recent advances in Rule-based machine learning models not only guarantee accurate stratification of individual risks but also enable automatic identification of interpretable risk predictive rules for describing the characteristics of different risk groups, thus holding great promise to inform policy design for clinical practice. However, the utility of Rule-based models on CVD risk prediction and personalized lifestyle recommendation has yet to be explored. Moreover, due to the complex interactions between lifestyle behaviors and other predictors, how to leverage the risk predictive rules for personalized lifestyle recommendation is a challenging problem. In this study, we are focusing on answering two main research questions. Firstly, we develop a Rule-based model to discover risk predictive rules associated with CVD, stratify individual risks and compare them with other machine learning models. Secondly, we developed a Rule-based personalized lifestyle recommendation algorithm to recommend the healthy lifestyle behaviors that help individual patients to decrease the risk of CVD. By applying the proposed methods on a national community study dataset, we demonstrate their effectiveness on CVD risk prediction, quality of the discovered rules and the efficiency of the recommended lifestyle modifications. The discovered rules hold great promise to advance our understanding of the pathology of CVD and allow for new guidelines to be developed for the lifestyle modification.



CVD prediction, Personalized lifestyle recommendation, Rule-based model