Explainable Machine Learning Models for Predicting Recurrence in Differentiated Thyroid Cancer

Abstract

Aim: Differentiated thyroid cancer (DTC) is a common type of cancer that originates in the thyroid gland. This study aimed to predict the recurrence of differentiated thyroid carcinoma, in patient with well-DTC, using explainable machine learning (XAI) models. Material and Methods: The study utilized a dataset from the UCI Machine Learning Repository, which included 383 patients and 13 candidate predictors. After a variable selection process using distance correlation, only four predictors (Response, Risk, T, and N) were retained for model building. Two XAI models, Fast Interpretable Greedy-Tree Sums (FIGS) and Explainable Boosting Machines (EBM), were employed. Results: The EBM model slightly outperformed the FIGS model in terms of accuracy. The study found that the most influential predictors of Well-DTC recurrence were the response to DTC treatment, risk status according to the American Thyroid Association classification, tumor size (T), and lymph node metastasis (N). Conclusion: In conclusion, this study successfully identified key risk factors for DTC recurrence using XAI models, providing interpretable insights for clinical decision-making and potential for personalized treatment strategies.