A CNN Model with Discretized Mobile Features for Depression Detection

Abstract

Depression has been a serious mental illness for a long time, which significantly influences people’s life quality. Meanwhile, as the smartphone becomes an integral part of people’s lives, it creates the opportunity to analyze users’ feelings through their phone usage and sensor data. However, previous studies mainly adopt machine-learning methods for depression detection, ignoring the sequential patterns hidden in them. In this study, we aim to monitor the symptoms of depression through sequential mobile data collected from phones and their sensors. First, we establish a deep-learning model called Dep-caser to fully utilize the sequential information in mobile data. Next, we introduce a discretization method based on Information Value to deal with data sparsity and outliers. In total, we recruited 257 people to join the study and extracted five-day longitudinal data from their smartphones and electronic bands. We conduct two experiments to examine the effectiveness of the Dep-caser and discretization method respectively. The results demonstrate that Dep-caser outperforms most of the machine learning methods and the discretization further improves the performance of the deep-learning model to achieve an overall accuracy of 0.83. Our study shows the promising future to adopt deep-learning models with sequential phone usage and sensing data to detect depression.