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

Formative Evaluation of Digital Health Interventions (2845) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (638) mHealth for Data Collection and Research (1087) Depression and Mood Disorders; Suicide Prevention (1527) Artificial Intelligence (1767) Diagnostic Tools in Mental Health (283) Machine Learning (1760)

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
Article Authors Cited by (10) Tweetations (3) Metrics

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, Radovic 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
  6. Schaab B, Calvetti P, Hoffmann S, Diaz G, Rech M, Cazella S, Stein A, Barros H, Silva P, Reppold C. How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review. Cadernos de Saúde Pública 2024;40(11) View
  7. Shui X, Xu H, Tan S, Zhang D. Depression Recognition Using Daily Wearable-Derived Physiological Data. Sensors 2025;25(2):567 View
  8. Mumenin N, Abu Yousuf M, Alassafi M, Mostafa Monowar M, Abdul Hamid M. DDNet: A Robust, and Reliable Hybrid Machine Learning Model for Effective Detection of Depression Among University Students. IEEE Access 2025;13:49334 View
  9. Souza E, Quirino M, Dendasck C, Dias C. Design centrado no humano aliado aos projetos de inteligência artificial para suporte na área de saúde mental e bem-estar: uma revisão sistemática. Revista Científica Multidisciplinar Núcleo do Conhecimento 2025:126 View

Books/Policy Documents

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