A modified single-stacked ensemble framework for prognostic analysis of leukemia relapse - Chuan, Zun Liang (IGI Global, 2026)
Bone marrow transplant (BMT) is a life-saving treatment for acute leukemia, though relapse remains a risk due to donor compatibility, clinical variability, and healthcare resource limitations. This chapter introduces a modified single-stacked ensemble prognostic algorithm optimized for interpretability, transparency, and reliability, especially for small and imbalanced datasets. While its predictive performance is average, the algorithm identifies key relapse-related covariates, including acute GVHD time, acute and chronic GVHD occurrence, platelet recovery time and status, and French-American-British (FAB) classification. By embedding structured statistical modeling within an AI framework, the approach integrates clinical insight with algorithmic reasoning. It promotes e-collaboration by enabling clinicians to engage with AI outputs, fostering trust and transparency. This study contributes to explainable AI in hematology, supports clinical decision-making, enhances patient care, and aligns with United Nations Sustainable Development Goal 3 (Good Health and Well-Being).