EXPLAINABLE BREAST CANCER DIAGNOSIS USING TUMOR MORPHOLOGY
DOI:
https://doi.org/10.69980/4qjfds31Keywords:
Breast cancer diagnosis, Tumor morphology, Explainable artificial intelligence, Machine learning, SHAP analysisAbstract
Concrete compressive strength is a critical indicator of structural performance and material quality, but Breast cancer remains a leading cause of cancer-related morbidity and mortality, emphasizing the need for accurate and interpretable diagnostic approaches. This study developed an explainable machine learning framework for breast cancer diagnosis using quantitative tumor morphology features derived from the Breast Cancer Wisconsin (Diagnostic) Dataset. Following data preprocessing and exploratory analysis, five machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, were trained and comparatively evaluated using standard classification metrics. Model interpretability was achieved through SHapley Additive exPlanations (SHAP), enabling both global and local interpretation of feature contributions. Among the evaluated models, Logistic Regression demonstrated the highest diagnostic performance, while SHAP analysis identified radius, perimeter, concavity, concave points, and texture as the most influential morphological predictors of malignancy. The explainability framework enhanced transparency by illustrating how individual features affected model predictions, thereby improving the reliability and clinical interpretability of automated diagnostic decisions. The proposed framework demonstrates that combining robust machine learning with explainable artificial intelligence can provide accurate, transparent, and clinically meaningful breast cancer diagnosis. These findings support the integration of explainable diagnostic models into clinical decision-support systems and establish a foundation for future research involving multicenter datasets and multimodal medical information.
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