Explainable and Uncertainty-Aware Ensemble Framework with Causal Analysis for Breast Cancer Detection

Muhammad Zaheer Sajid¹Muhammad Fareed Hamid²Imran Qureshi^3*

• ¹Department of Electrical and Computer Science, University of Missouri, Columbia, United States
• ²Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad, Pakistan
• ³College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

Breast cancer is one of the main causes of cancer deaths around the world and is known for its aggressive growth and ability to spread. While machine learning has shown good results for diagnosis, most existing methods do not handle uncertainty or explain their predictions clearly. In this study we present an integrated framework that combines uncertainty aware ensemble learning with causal feature analysis and multi modal explainability for breast cancer prediction. The framework uses a mix of LightGBM, Random Forest, and Gradient Boosting classifiers that include uncertainty estimation so the model can mark predictions that are less confident. It also applies causal analysis to detect possible clinical confounders and uses SHAP, permutation importance, and feature attribution for interpretation. Tests on two public datasets showed strong and consistent performance. On the UCTH Clinical Dataset the model reached an AUC of 0.97%, accuracy of 0.95%, and F1 score of 0.94%, with 100% precision for high confidence cases and no false positives. On the Breast Cancer Wisconsin dataset it achieved an AUC of 0.99, accuracy of 0.94%, and F1 score of 0.92%, which increased to 0.98% accuracy and 0.98% F1 score when only certain predictions were considered. Causal analysis pointed out important clinical confounders like lymph node involvement, tumor size, and metastasis, while fairness tests showed balanced results across demographic groups. Overall the framework combines uncertainty estimation and causal interpretability to give predictions that are both accurate and trustworthy. It provides clinicians with clear confidence levels for every prediction and supports transparent decision making that can reduce diagnostic errors and improve reliability in clinical use.