This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (191) Mobile Health in Psychiatry (426) Methods and New Tools in Mental Health Research (454) Machine Learning (1465) Diagnostic Tools in Mental Health (242)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26540, first published .
Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

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

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

Authors of this article:

Kennedy Opoku Asare1 Author Orcid Image ;   Yannik Terhorst2 Author Orcid Image ;   Julio Vega3 Author Orcid Image ;   Ella Peltonen1 Author Orcid Image ;   Eemil Lagerspetz4 Author Orcid Image ;   Denzil Ferreira1 Author Orcid Image
Article Authors Cited by (58) Tweetations (6) Metrics

Kennedy Opoku Asare   1 , MSc ;   Yannik Terhorst   2 , MSc ;   Julio Vega   3 , PhD ;   Ella Peltonen   1 , PhD ;   Eemil Lagerspetz   4 , PhD ;   Denzil Ferreira   1 , PhD

1 Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

2 Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany

3 Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States

4 Department of Computer Science, University of Helsinki, Helsinki, Finland

Corresponding Author:

  • Kennedy Opoku Asare, MSc
  • Center for Ubiquitous Computing
  • University of Oulu
  • Erkki Koiso-Kanttilan katu 3
  • PO Box 4500
  • Oulu, FI-90014
  • Finland
  • Phone: 358 294 482807
  • Fax: 358 8 553 2612
  • Email: kennedy.opokuasare@oulu.fi