An Optimized Survival Prediction Method for Kidney Transplant Recipients

Human organ transplantation is a lifesaving process for many of the patients suffering from end stage diseases. Transplantation surgeons are often confronted with the question of the expected survival prognosis for this expensive and perilous process.The aim of the work is to identify an optimal model for predicting the survival of the recipient based on the available organ. This study identifies important features of the recipient and donor parameters for training the model. The study compares the performance of the Random Survival Forest (RSF), which is a machine learning method, and the Cox Proportional Hazard (CPH) model, which is a statistical model, to identify the more accurate model for survival prediction. Variations of the C-index, Brier score, and cumulative Area Under Curve evaluate the survival models considered. This study suggests that CPH which is a statistical method is a better option for forecasting graft and patient survival for an improved clinical outcome.

@article{Chalissery2023,
  title     = {An Optimized Survival Prediction Method for Kidney Transplant Recipients},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Benita Jose Chalissery and V. Asha},
  doi       = {10.14569/IJACSA.2023.0140983},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140983}
}