Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

•A novel model predicts early antibody-incompatible kidney transplant rejection.•The models were trained on a small dataset of pre-transplant characteristics.•Decision Tree and Random Forest classifiers achieved 85% accuracy.•The models identified key risk factors, including specific IgG subclass levels.