March 14, 2025European Journal of RadiologyOpen Access

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis

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Abstract

Our XGBoost model, supported by SHAP analysis empowers clinicians with interpretable insights and defined risk thresholds and could facilitate informed decision-making and patient-centric care.

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Schindele et al. (2025) studied this question.

synapsesocial.com/papers/69dc4b69d50c49528a9f5677 https://doi.org/10.1016/j.ejrad.2025.112049

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Abstract

Our XGBoost model, supported by SHAP analysis empowers clinicians with interpretable insights and defined risk thresholds and could facilitate informed decision-making and patient-centric care.

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis

Abstract

Discussion

Cite This Study

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis

Abstract

Discussion

Cite This Study

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