Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments

Depression affects an estimated 300 million people around the globe. Early detection of depression and associated mental health problems constitutes one of the best prevention methods when trying to reduce the disease’s incidence. Information collected by tracking smartphone use behaviour and using ecological momentary assessments (EMA) can be used together with machine learning techniques to identify patterns indicative of depression and predict its appearance, contributing in this way to its early detection. However many of these techniques fail to identify the importance and relationships between the factors used to reach their prediction outcome. In this paper we propose the use of Bayesian networks (BN) as a tool to analyse and model data collected using EMA and smartphone measured behaviours. We compare the performance of BN against results obtained using support vector regression and random forest. The comparison is done in terms of efficacy, efficiency, and insight. Results show that no significant difference in efficacy was found between the models. However, BN presented clear advantages in terms of efficiency and insight given its probability factorization, graphical representation and ability to infer under uncertainty.