Research Article

Machine learning models for real-time depression prediction

Volume: 17 June 23, 2026
TR EN

Machine learning models for real-time depression prediction

Abstract

Depression is a pervasive global health challenge affecting millions worldwide, and its timely diagnosis remains critical for effective intervention. Traditional diagnostic approaches predominantly rely on subjective clinical assessments, which often lead to delayed detection and suboptimal treatment outcomes. This study investigates the integration of artificial intelligence (AI) with wearable technology to enable continuous, objective prediction of depression severity in real time. Leveraging physiological and behavioral data collected from wearable devices, advanced machine learning algorithms—including Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GB)—were applied to classify depression severity. Initially, the classification framework comprised five categories but was subsequently streamlined to three classes to improve model performance. Evaluation using accuracy and Mean Squared Error (MSE) metrics revealed that the Gradient Boosting model consistently outperformed other approaches. These findings underscore the transformative potential of AI-enabled wearable platforms to facilitate early, accurate depression detection, thereby enhancing personalized mental health care and treatment outcomes.

Keywords

References

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Details

Primary Language

English

Subjects

Computing Applications in Health, Stream and Sensor Data, Modelling and Simulation

Journal Section

Research Article

Publication Date

June 23, 2026

Submission Date

July 31, 2025

Acceptance Date

April 21, 2026

Published in Issue

Year 2026 Volume: 17

APA
Osmanoğlu, H., & Gismalseed, M. E. D. A. (2026). Machine learning models for real-time depression prediction. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 17. https://doi.org/10.28948/ngumuh.1754432
AMA
1.Osmanoğlu H, Gismalseed MEDA. Machine learning models for real-time depression prediction. NOHU J. Eng. Sci. 2026;17. doi:10.28948/ngumuh.1754432
Chicago
Osmanoğlu, Hüsamettin, and Moneeb Elamin Dafa Alla Gismalseed. 2026. “Machine Learning Models for Real-Time Depression Prediction”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 17 (June). https://doi.org/10.28948/ngumuh.1754432.
EndNote
Osmanoğlu H, Gismalseed MEDA (June 1, 2026) Machine learning models for real-time depression prediction. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 17
IEEE
[1]H. Osmanoğlu and M. E. D. A. Gismalseed, “Machine learning models for real-time depression prediction”, NOHU J. Eng. Sci., vol. 17, June 2026, doi: 10.28948/ngumuh.1754432.
ISNAD
Osmanoğlu, Hüsamettin - Gismalseed, Moneeb Elamin Dafa Alla. “Machine Learning Models for Real-Time Depression Prediction”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 17 (June 1, 2026). https://doi.org/10.28948/ngumuh.1754432.
JAMA
1.Osmanoğlu H, Gismalseed MEDA. Machine learning models for real-time depression prediction. NOHU J. Eng. Sci. 2026;17. doi:10.28948/ngumuh.1754432.
MLA
Osmanoğlu, Hüsamettin, and Moneeb Elamin Dafa Alla Gismalseed. “Machine Learning Models for Real-Time Depression Prediction”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 17, June 2026, doi:10.28948/ngumuh.1754432.
Vancouver
1.Hüsamettin Osmanoğlu, Moneeb Elamin Dafa Alla Gismalseed. Machine learning models for real-time depression prediction. NOHU J. Eng. Sci. 2026 Jun. 1;17. doi:10.28948/ngumuh.1754432