Üniversite Öğrencilerinde Stres Düzeyinin Makine Öğrenmesiyle Tahmini: Sentetik Veri Destekli Optimizasyon Yaklaşımı

Öz

Üniversite öğrencileri, akademik baskı ve sosyal zorluklar nedeniyle yüksek stres riski taşımaktadır. Bu çalışma, 2.000 öğrenciden elde edilen öz-bildirim temelli yaşam tarzı verilerini kullanarak stres düzeylerini makine öğrenmesi ile tahmin etmeyi amaçlamaktadır. Veri setindeki sınıf dengesizliği, eğitim aşamasında Koşullu Tablo Üretici Üretken Karşıt Ağ yöntemiyle üretilen sentetik verilerle giderilmiş; modeller tabakalı çapraz doğrulama ile test edilmiştir. Karşılaştırılan beş algoritma arasında Rastgele Orman modeli hem dengeli hem dengesiz veri setlerinde tüm örnekleri doğru sınıflandırarak en yüksek performansa ulaşmıştır (Doğruluk = 1.00; AUC = 1.00; Makro F1 = 1.00). Modelin karar mekanizması Shapley analizi ile incelenmiş; "Günlük Çalışma Süresi" ve "Uyku Süresi" en belirleyici faktörler olarak saptanmıştır. Elde edilen yüksek başarımın veri sızıntısı ya da rastlantısal uyumdan kaynaklanma olasılığı, bağımsız sınama kümesinin süreç boyunca ayrı tutulması ve sağlamlık kontrolleri olarak uygulanan ablasyon (özellik çıkarma) ile etiket permütasyon testi bulguları ile desteklenmiştir. Sonuçlar, sentetik veriyle desteklenen ve açıklanabilir yapay zeka ile doğrulanan modellerin, öğrencilerin stres düzeylerinin erken tespitinde güvenilir bir araç olduğunu göstermektedir.

Anahtar Kelimeler

• Stres düzeyi tahmini
• makine öğrenmesi
• sentetik veri
• eğitim psikolojisi

Predicting Stress Levels in University Students Using Machine Learning: An Optimization Approach Based on Synthetic Data

Öz

University students are at high risk of stress due to academic pressure and social challenges. This study aims to predict stress levels with machine learning using self-report-based lifestyle data from 2,000 students. The class imbalance in the dataset was eliminated in the training phase with synthetic data generated by the Conditional Table Generating Generative Adversarial Network method, and the models were tested with stratified cross-validation. Among the five algorithms compared, the Random Forest model achieved the highest performance (Accuracy = 1.00; AUC = 1.00; Macro F1 = 1.00), correctly classifying all instances in both balanced and imbalanced data sets. The decision mechanism of the model was analyzed by Shapley analysis; “Daily Working Time” and “Sleeping Time” were found to be the most determining factors. The possibility that the high performance obtained was due to data leakage or random fit was supported by the fact that the independent test set was kept separate throughout the process and the findings of the ablation (feature extraction) and label permutation tests applied as robustness checks. The results suggest that models supported by synthetic data and validated by explainable artificial intelligence are a reliable tool for early detection of students' stress levels.

Anahtar Kelimeler

• Stress level prediction
• machine learning
• synthetic data
• educational psychology

Kaynakça

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