Thyroid Cancer Recurrence Prediction: A Comparison of Synthetic and Real Data

Öz

Data scarcity and/or privacy constraints in medical research and many other research fields pose obstacles to the development of artificial intelligence (machine learning, deep learning) models for various studies. In this study, it has been demonstrated how synthetic data generation can provide a solution for augmenting small datasets. The Differentiated Thyroid Cancer Recurrence Dataset obtained from the UCI Machine Learning Repository was used. Large-scale synthetic data preserving the statistical characteristics of the original data was generated and class imbalance was improved. Performance evaluation was conducted with XGBoost, LightGBM, k-Nearest Neighbors (kNN) and Decision Tree (DT) algorithms. Looking at the results, it was revealed that models trained with synthetic data showed comparable or better performance than original data. Consistent performance was obtained in ensemble methods, while significant improvements were achieved in simple models. Stability analysis showed that XGBoost and LightGBM were the most consistent models.

Anahtar Kelimeler

• Synthetic data
• Thyroid cancer
• Recurrence prediction
• Machine learning
• Privacy preservation
• Medical artificial intelligence

Tiroit Kanseri Nüks Tahmini: Sentetik ve Gerçek Veri Karşılaştırması

Öz

Tıbbi araştırmalar başta olmak üzere diğer birçok araştırma dallarında veri kıtlığı ve/veya gizlilik kısıtlamaları, çeşitli çalışmalar için yapay zekâ (makine öğrenmesi, derin öğrenme) modellerinin geliştirilmesinde engeller oluşturmaktadır. Bu çalışmada, sentetik veri üretiminin küçük veri setlerini büyütmede nasıl bir çözüm üretebileceği gösterilmiştir. UCI Makine Öğrenmesi Deposu'ndan elde edilen Farklılaşmış Tiroit Kanseri Nüks Veri Seti kullanılmıştır. Orijinal verinin istatistiksel özelliklerini koruyan büyük ölçekli sentetik veri üretilmiş ve sınıf dengesizliği iyileştirilmiştir. XGBoost, LightGBM, k-En Yakın Komşu (kEYK) ve Karar Ağacı (KA) algoritmaları ile performans değerlendirmesi yapılmıştır. Sonuçlara bakıldığında, sentetik veri ile eğitilen modellerin orijinal veri ile karşılaştırılabilir veya daha iyi performans gösterdiği ortaya çıkmıştır. Topluluk yöntemlerinde tutarlı performans, basit modellerde ise kayda değer iyileşmeler elde edilmiştir. Kararlılık analizi, XGBoost ve LightGBM'in en tutarlı modeller olduğunu göstermiştir.

Anahtar Kelimeler

• Sentetik veri
• Tiroit kanseri
• Nüks tahmini
• Makine öğrenmesi
• Gizlilik koruma
• Tıbbi yapay zekâ

Kaynakça

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