Discovery of Interpretable Patterns of Breast Cancer Diagnosis via Class Association Rule Mining (CARM) With SHAP-Based Explainable AI (XAI)
DOI:
https://doi.org/10.11113/mjfas.v21n3.3792Keywords:
Breast cancer, class association rule, pattern discovery, SHAP, XAI model.Abstract
Breast cancer remains the most common cancer among women globally highlighting the importance of early and reliable diagnostic methods. While previous studies have applied association rules mining (ARM) to explore factors contributing to breast cancer, many lacked robust validation of the extracted rules. To address this gap and deepen our understanding of the key biological markers linked to the disease, this study proposes a hybrid framework that integrates Class Association Rule Mining (CARM) with SHapley Additive exPlanations (SHAP) values based on Random Forest (RF) and Gradient Boost (GB) models to uncover and validate meaningful diagnostic patterns. Using the Breast Cancer Coimbra (BCC) dataset comprising 116 patient samples and nine biological markers, a total of 723,938 association rules (AR) were generated with 17,720 significant class association rules (CAR) were extracted. These rules were pruned using lift, leverage and conviction to retain the most relevant ones. Among the healthy group, combinations involving low glucose, low insulin, low resistin and low Homeostatic Model Assessment (HOMA) were consistently observed, while high BMI appeared particularly among younger individuals. These features were associated with negative SHAP values validating their contribution to healthy classifications. In contrast, common patterns such as high glucose, medium resistin and medium Monocyte Chemoattractant Protein-1 (MCP.1) among middle aged individuals highlighting their influence in predicting patient classification. These features consistently showed strong positive SHAP values across both classifiers highlighting their influence in predicting patient outcomes. By combining rule extraction of CARM with feature contribution using SHAP, this study provides a validated and interpretable approach for breast cancer diagnosis. The findings highlight the importance of feature interactions and offer promising directions for personalized risk assessment and early detection.
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