Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques

Background: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), relapse, and graft-versus-host disease (GVHD).

Objective: This study aims to evaluate the capability of machine learning (ML) models in predicting OS, relapse, and GVHD in AL patients post-allo-HSCT.

Methods: Clinical data from 1243 AL patients, with 10 years of follow-up, was utilized to develop 28 ML models. These models incorporated four feature selection methods and seven ML algorithms. Model performance was assessed using the concordance index (c-index) with multivariate analysis.

Results: The multivariate model analysis showed the best FS/ML combinations were UCI_GLMN, IBMA_GLMN and IBMA_CB for OS, UCI_ST, UCI_RSF, UCI GLMB, UCI_GB, UCI_CB, MI_GLMN, IBMA_ST and IBMA GB for relapse, IBMA_GB for aGVHD and Boruta_GB for cGVHD (all p values < 0.0001, mean C-indices in 0.61-0.68)).

Conclusion: ML techniques, when combined with clinical variables, demonstrate high accuracy in predicting OS, relapse, and GVHD in AL patients.