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

Clinical datasets are commonly limited in size, thus restraining applications of Machine Learning (ML) techniques for predictive modelling in clinical research and organ transplantation. We explored the potential of Decision Tree (DT) and Random Forest (RF) classification models, in the context of small dataset of 80 samples, for outcome prediction in high-risk kidney transplantation. The DT and RF models identified the key risk factors associated with acute rejection: the levels of the donor specific IgG antibodies, the levels of IgG4 subclass and the number of human leucocyte antigen mismatches between the donor and recipient. Furthermore, the DT model determined dangerous levels of donor-specific IgG subclass antibodies, thus demonstrating the potential of discovering new properties in the data when traditional statistical tools are unable to capture them. The DT and RF classifiers developed in this work predicted early transplant rejection with accuracy of 85%, thus offering an accurate decision support tool for doctors tasked with predicting outcomes of kidney transplantation in advance of the clinical intervention.