Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis : Exploratory Study

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dc.contributor.author Asare, Kennedy Opoku
dc.contributor.author Terhorst, Yannik
dc.contributor.author Vega, Julio
dc.contributor.author Peltonen, Ella
dc.contributor.author Lagerspetz, Eemil
dc.contributor.author Ferreira, Denzil
dc.date.accessioned 2021-09-16T04:31:01Z
dc.date.available 2021-09-16T04:31:01Z
dc.date.issued 2021-07
dc.identifier.citation Asare , K O , Terhorst , Y , Vega , J , Peltonen , E , Lagerspetz , E & Ferreira , D 2021 , ' Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis : Exploratory Study ' , JMIR mhealth and uhealth , vol. 9 , no. 7 , 26540 . https://doi.org/10.2196/26540
dc.identifier.other PURE: 168504432
dc.identifier.other PURE UUID: 8baaed64-5fa6-4acd-94fd-382804cb6ddc
dc.identifier.other WOS: 000692249000020
dc.identifier.other ORCID: /0000-0003-3875-8135/work/100025334
dc.identifier.uri http://hdl.handle.net/10138/334391
dc.description.abstract Background: Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. Objective: The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. Methods: Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. Results: Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants' age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score = 10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status-normalized entropy and depression (r=0.14, P Conclusions: Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors' data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring. en
dc.format.extent 17
dc.language.iso eng
dc.relation.ispartof JMIR mhealth and uhealth
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject mHealth
dc.subject mental health
dc.subject mobile phone
dc.subject digital biomarkers
dc.subject digital phenotyping
dc.subject smartphone
dc.subject supervised machine learning
dc.subject depression
dc.subject FALSE DISCOVERY RATE
dc.subject MENTAL-HEALTH
dc.subject MUTUAL INFORMATION
dc.subject HEART-FAILURE
dc.subject IMPUTATION
dc.subject SYMPTOMS
dc.subject PATTERNS
dc.subject SLEEP
dc.subject 113 Computer and information sciences
dc.title Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis : Exploratory Study en
dc.type Article
dc.contributor.organization Department of Computer Science
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.2196/26540
dc.relation.issn 2291-5222
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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