UC San Diego

Hypertension, or high blood pressure (BP), is one of the most prevalent chronic diseases in the world, affecting 30% of American adults and contributing to over 410,000 deaths per year. Although the effectiveness of lifestyle interventions on BP management has been proven in many studies, the degree of impact of each lifestyle factor on BP is unknown and may vary significantly between individuals. In this dissertation, we propose to use machine learning techniques to elucidate the complex relationships between BP and lifestyle factors at the level of the individual. Based on the continuous data collected from wearables of users, we aim to 1) provide a prediction of BP, which will give users a quick and reliable way to understand their health condition, and 2) provide personalized and actionable insight to users in order to control their BP by adjusting their lifestyle accordingly.Firstly, we extract necessary and interpretable features from lifestyle data. To utilize temporal information from the BP series, we propose to extract new features based on ARIMA to enhance the accuracy of BP prediction. We propose a machine learning method to explore the personalized relationship between BP and lifestyle factors collected by wearables. The proposed system provides BP prediction as well as lifestyle recommendations. 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. Thirdly, we propose a feature selection method using feature importance derived from Shapley Value, in order to remove redundant and irrelevant features and provide the personalized insight that may affect an individual’s BP. We evaluate and show that our proposed technique outperforms other popular machine learning methods in terms of prediction error. We also show the effectiveness of personalized recommendations using a randomized controlled trial. In the final section, earlier work in green communication, which utilize renewable energy to reduce grid energy consumption of base station (BSs), is presented. We propose to utilize data buffer of user equipment as well as energy storage at the BS to better adapt the BS resource allocation and hence its energy consumption to the dynamic nature of RE.