Convolutional regression framework for health behavior prediction

Understanding the propagation of human health behavior, such as smoking and obesity, and identification of the factors that control such phenomenon is an important area of research in recent years mainly because, in industrialized countries a substantial proportion of the mortality and quality of life is due to particular behavior patterns, and that these behavior patterns are modifiable. Predicting the individuals who are going to be overweight or obese in future, as overweight and obesity propagate over dynamic human interaction network, is an important problem in this area. However, the problem has received limited attention from the network analysis and machine learning perspective till date. In this work, we propose a scalable supervised prediction model based on convolutional regression framework that is particularly suitable for short time series data. We propose various schemes to model social influence for health behavior change. Further we study the contribution of the primary factors of overweight and obesity, like unhealthy diets, recent weight gains and inactivity in the prediction task. A thorough experiment shows the superiority of the proposed method over the state-of-the-art.