arch curr med res

Archives of Current Medical Research

2717-9788

14 Mart Tıbbiyeliler Derneği

10.47482/acmr.1677545

Nuclear Medicine Radiology and Organ Imaging

Nükleer Tıp Radyoloji ve Organ Görüntüleme

Diferansiye tiroid kanseri nüksünün açıklanabilir yapay zeka modeli ile tahmin edilmesi

Predicting recurrence of differentiated thyroid cancer with an explainable artificial intelligence model

https://orcid.org/0000-0002-7082-6479

Öztürk

Ahmet Cankat

T.C. Cumhurbaşkanlığı Savunma Sanayii Başkanlığı

https://orcid.org/0000-0001-5573-5096

Akkur

Erkan

Türkiye İlaç ve Tıbbi Cihaz Kurumu

https://orcid.org/0000-0001-6525-5125

Çizmecioğullari

Serkan

KIRŞEHİR AHİ EVRAN ÜNİVERSİTESİ

09 28 2025

6 3 280 287 04 16 2025 06 02 2025

2020

Archives of Current Medical Research

Amaç: Bu çalışmanın amacı, diferansiye tiroid kanserinin nüksünü tahmin etmek ve açıklanabilir yapay zeka modeli aracılığıyla nüksün en temsili özelliklerini belirlemektir.Gereç ve yöntemler: Veri kümesi UCI Makine Öğrenimi Deposundan elde edilmiş ve “Diferansiye Tiroid Kanseri Nüksü” olarak adlandırılmıştır. Bu veri kümesi 383 hasta ve 17 özellik içermektedir. Diferansiye tiroid kanserinin nüksünü tahmin etmek için Random Forest, Gradient Boosting, AdaBoost, Support Vector Classifier ve Logistic Regression olmak üzere beş sınıflandırıcı kullanılmıştır. Tahmin sonucu üzerinde en fazla etkiye sahip özellikleri belirlemek için permütasyon özellik önemi ve Shapley açıklanabilir yapay zeka yöntemleri kullanılmıştır.Sonuçlar: Random Forest algoritması sırasıyla %0,9739 ve 0,993 ile en yüksek doğruluk ve AUC-ROC skorunu elde etmiştir. En belirgin risk faktörleri tedavi yanıtı, ATA riski, tümör ve yaş olarak belirlenmiştir. Sonuç: Önerilen açıklanabilir makine öğrenimi modeli, diferansiye tiroid kanserinin nüksünü tahmin etmek ve en belirgin özellikleri belirlemek için güvenilir bir model olarak kabul edilebilir. Bu model, sağlık profesyonellerine yardımcı olma potansiyeline sahip olabilir.

Background: This study aimed to predict the recurrence of differentiated thyroid cancer and identify its most representative risk factors using an explainable artificial intelligence model.Methods: The publicly available Differentiated Thyroid Cancer Recurrence dataset from the University of California Irvine Machine Learning Repository, comprising 383 patients and 17 features, was employed. Five classifiers, -Random Forest, Gradient Boosting, AdaBoost, Support Vector Classifier and Logistic Regression-, were employed to predict the recurrence. Permutation feature importance (PFI) and SHapley Additive exPlanations (SHAP) explainable artificial intelligence methods were used to determine the features that had the most impact on the prediction result.Results: The Random Forest algorithm outperformed others, achieving an accuracy of 97.39% and an Area under the Curve of 0.993. Response to treatment, ATA risk stratification, tumor stage and patient age were determined as the factors with the highest contribution to the model prediction process through SHAP and permutation importance analyses, and this finding was consistent with the prognostic markers stated in the literature.Conclusion: The proposed explainable machine learning framework has shown satisfactory results in predicting DTC recurrence while identifying clinically important features. This approach can offer valuable support to clinicians in early identification of high-risk patients and personalization of surveillance strategies.

Differentiated thyroid cancer recurrence machine learning explainable artificial intelligence shapley permutation feature importance

Diferansiye tiroid kanseri nüks makine öğrenmesi açıklanabilir yapay zeka shapley permütasyon özellik önemi

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