Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method

Abstract

Background. Kidney transplantation is the best alternative treatment for end-stage renal disease (ESRD). In order to optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival. Methods. Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling (SMOTE) and random under sampling (RUS) were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches are employed (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach. Results. 34 out of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level (“Recipient creatinine at discharge”, “Recipient dialysis time”, “Donor history of diabetes”, “Donor kidney biopsy” and “Donor cause of death”),17 variables in important level, and 12 variables in low important level. Conclusion. In this study we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.