The terms entropy and robustness, are now being used by biomedical investigators to know about the risk of the change in system in future. The former is the mathematical identification of uncertainty about the systems, while the later, is defined as the likelihood of system stability. We aimed to report an entropy based analysis of our renal transplantation data set. Moreover, we used the index of relative CoV (Coefficient of Variation) to measure the responsiveness level of medical (or curing) process to our input variables variation.
Material and methods: We designed a model consisting of input variables comprised of demographic variables of donor and recipients, past medical history, and further clinical data. Output variables included 6 months, 1 and 2 years patient and graft survival. Data-entropy analysis was done by using software Ontonix s.r.l. (www.ontonix.com). Our data showed the total input and output entropy as 13.14 and 1.54, the mean input and output robustness as 39.14%, and 29.54%, and the robustness amplification index as 0.75. Minimum entropy of inputs were reported for MI hx (0.07), vascular disease hx (0.1), bladder remaining urine (0.13) and urologic surgery hx (0.15). The Minimum entropy of output variables were 0.20 and 0.22, 0.25 and 0.27, and 0.28 and 0.32 for 6 months and1 year patient survival, 6 months and 1 year graft survival, 2 year patient and graft survival, respectively. This analysis highlights the heterogenic certainty of various variables in our transplantation data set. Within our output variables, in comparison to those in 6 months, future 2 years patient and graft survival seems to be more unpredictable. Also, the CoV s suggest that the undertaken medical system is strictly irresponsive to the variation of input measures. This technique can be used for statistic experts who work in the field of transplantation in other MESOT countries.