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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Machine learning models for real-time depression prediction

Gerçek zamanlı depresyon tahmini için makine öğrenmesi modelleri

Hüsamettin Osmanoğlu(ISTINYE UNIVERSITY)0000-0002-1027-2945
Moneeb Elamin Dafa Alla Gismalseed(istinye University)0009-0001-7944-3028
Pages
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Publication Date
2026-06-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis:Depression is a pervasive global health challenge affecting millions worldwide, and its timely diagnosis remains critical for effective intervention.
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Key Findings & Data:Initially, the classification framework comprised five categories but was subsequently streamlined to three classes to improve model performance.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Depresyon Tahmini · Makine Öğrenmesi · LSTM

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.

Özet

Depresyon, dünya genelinde milyonlarca insanı etkileyen yaygın bir küresel sağlık sorunudur ve zamanında teşhisi, etkili müdahaleler için kritik öneme sahiptir. Geleneksel tanı yöntemleri çoğunlukla öznel klinik değerlendirmelere dayanmakta olup, bu durum sıklıkla geç teşhis ve yetersiz tedavi sonuçlarına yol açmaktadır. Bu çalışma, yapay zekâ (YZ) ile giyilebilir teknolojinin entegrasyonunu araştırarak depresyon şiddetinin gerçek zamanlı, sürekli ve nesnel olarak tahmin edilmesini sağlamayı amaçlamaktadır. Giyilebilir cihazlardan toplanan fizyolojik ve davranışsal veriler kullanılarak, Uzun Kısa Süreli Bellek (LSTM), Rastgele Orman (RF) ve Gradyan Artırma (GB) gibi gelişmiş makine öğrenmesi algoritmaları depresyon şiddetini sınıflandırmak için uygulanmıştır. Başlangıçta sınıflandırma beş kategori üzerinden yapılmış, ancak model performansını artırmak amacıyla daha sonra üç sınıfa indirgenmiştir. Doğruluk (accuracy), ve Ortalama Kare Hatası (MSE) gibi değerlendirme metrikleri, Gradient Boosting modelinin diğer yaklaşımlara kıyasla tutarlı şekilde üstün performans gösterdiğini ortaya koymuştur. Bu bulgular, yapay zekâ destekli giyilebilir platformların erken ve doğru depresyon tespiti sağlayarak kişiselleştirilmiş ruh sağlığı bakımı ve tedavi sonuçlarını iyileştirme konusunda dönüştürücü bir potansiyele sahip olduğunu vurgulamaktadır.

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Cite This Article

Osmanoğlu, H., Gismalseed, M. E. D. A. (2026). Machine learning models for real-time depression prediction. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1754432

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