Abstract
Predicting 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.
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