Offline and Online Learning Techniques for Personalized Blood Pressure Prediction and Health Behavior Recommendations

Abstract

Blood pressure (BP) is one of the essential indicators of human health and highly correlated to health behavior (e.g., exercise and sleep). However, the degree of impact of each health behavior on BP is unknown and may vary significantly between individuals. In this paper, we investigate the relationship between BP and health behavior using data collected from off-the-shelf wearable devices and wireless home BP monitors. We propose a personalized BP model based on random forest (RF), which can predict individual's BP using health behavior and historical BP, and identify the most important factors in predicting an individual's BP. The latter can be used to provide personalized health behavior recommendations to improve and manage BP. We propose RF with Feature Selection (RFFS), which performs RF-based feature selection to enhance the prediction. Furthermore, since BP and health behavior data are collected and learned sequentially, the performance of prediction is prone to the existence of concept drifts and anomaly points. To solve this problem, we propose an Online Weighted-Resampling (OWR) technique to enhance RFFS in an online learning scenario. To show the effectiveness of RFFS and OWR, we use existing machine learning methods on the proposed dataset as a comparison. Our experimental results show that the proposed approach achieves the lowest prediction error. We also validate the effectiveness of personalized recommendations based on feature importance, which influences the user to change lifestyle to improve his/her BP.

Keywords

• Machine learning
• data mining
• prediction methods
• smart healthcare