Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28848, first published .
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

Authors of this article:

Md Sabbir Ahmed1 Author Orcid Image ;   Nova Ahmed1 Author Orcid Image

Journals

  1. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  2. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  3. Turjo M, Mundada K, Haque N, Ahmed N. Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study. JMIR Research Protocols 2024;13:e55511 View
  4. Azad M, Leeon S, Khan R, Mohammed N, Momen S. SAD: Self-assessment of depression for Bangladeshi university students using machine learning and NLP. Array 2025;25:100372 View
  5. Todd E, Orr R, Gamage E, West E, Jabeen T, McGuinness A, George V, Phuong-Nguyen K, Voglsanger L, Jennings L, Angwenyi L, Taylor S, Khosravi A, Jacka F, Dawson S. Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review. Computers in Biology and Medicine 2025;185:109521 View

Books/Policy Documents

  1. Dey S, Singh K. Exploring the Micro World of Robotics Through Insect Robots. View