Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments

M. Rebolledo, A.E. Eiben, T. Bartz-Beielstein

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Abstract

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.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings
EditorsPedro A. Castillo, Juan Luis Jiménez Laredo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages373-387
Number of pages15
ISBN (Electronic)9783030726997
ISBN (Print)9783030726980
DOIs
Publication statusPublished - 2021
Event24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021 - Virtual, Online
Duration: 7 Apr 20219 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12694 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on the Applications of Evolutionary Computation, EvoApplications 2021 held as Part of EvoStar 2021
CityVirtual, Online
Period7/04/219/04/21

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