Oztekin, Asil2023-07-212023-07-212012https://repositorio.pucp.edu.pe/index/handle/123456789/194814Predicting the performance of planned organ transplantation has proved to be a critical problem to solve. The purpose of this study is to present a data mining-based model for variable filtering and selection in order to predict the performance of thoracic transplantation via the graft survivability after the transplant. To this end, 10-fold cross-validated information fusion-based sensitivity analyses on machine learning models are conducted to receive an unbiased predictor variable ranking to be used in a subsequent Cox survival analysis. The study is unique in that it provides a mathematical means for medical experts to deal with thoracic recipients more efficiently and effectively.enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0Prediction modelUnited Network for Organ Sharing (UNOS)Machine learninginfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#5.02.04