aceExplainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support
Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately higher mortality rates in low- and middle-income countries (LMICs) where diagnostic delays and limited specialist pathology capacity are widespread. While machine learning (ML) approaches achieve strong predictive performance for cancer classification, algorithmic opacity and absence of interpretability frameworks tailored to resource-constrained environments have impeded clinical adoption. This study bridg…
Explainable ML models achieved near-perfect discrimination for breast cancer diagnosis on cytology data, with top models reaching 0.996 AUC and 98.25% accuracy, supporting use in resource-constrained diagnostic workflows.
Algorithmic opacity and lack of interpretability frameworks tailored to resource-constrained environments have impeded clinical adoption of ML for breast cancer, contributing to diagnostic delays in settings with limited pathology capacity.
Evidence
- Peer-reviewedPLOS Digital Health2026-09-11
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Truvace Impact Record TRV-2026-1071, v1: “Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support.” Truvace, 2026-09-13. /record/TRV-2026-1071 (accessed at citation time). sha256 937490ee99b6b5d6…
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