Recurrent Neural Networks Based Obesity Status Prediction Using Activity Data

摘要

Obesity, a serious public health concern worldwide, increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers collect diverse types of data, which includes biomedical, behavioral and activity, and utilize machine learning techniques to mine hidden patterns for obesity status improvement prediction. While existing machine learning methods such as Recurrent Neural Networks (RNNs) provide exceptional results, it is challenging to discover hidden patterns of the sequential data due to the irregular observation time instances. Meanwhile, the lack of understanding of why those learning models are effective also limits further improvements on their architectures. Thus, we develop a RNN based time-aware architecture to handle irregular observation times and identify relevant feature extractions from longitudinal patient records for obesity status improvement pre-diction. Evaluations of real-world data involving activity data collected from wearables and electronic health records demonstrate that our proposed method can capture the underlying structures in users' time sequences with irregularities, and achieve an accuracy of 77% in predicting the obesity status improvement.