Surrogate Decision Trees for Explaining Black Box Models for Breast Cancer Classification
Year: 2026
Type: conference-paper
Abstract: Machine learning models can be beneficial for early diagnosis in healthcare sector. However, many of these models operate as black boxes, limiting their transparency and trustworthiness. To the best of author’s knowledge, this paper proposes a first-time application of a surrogate decision trees to explain the prediction of black-box model for breast cancer classification. Two different black-box models, K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN), were used on the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets, respectively. The proposed framework uses surrogate fidelity score, Spearman rank correlation, top-feature overlap, and stability analysis to quantify interpretability and explainability of the black-box model. Experimental results demonstrate that surrogate models can effectively approximate with a fidelity score of 0.9486 for KNN model and 0.9510 for ANN model. Also, a strong alignment between surrogate and SHAP explanations is observed for the KNN model, while moderate alignment is found for the ANN model, highlighting the challenges of explaining highly nonlinear systems.
Institutions: University of Portsmouth, Technical University of Sofia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7202128102