The article raises a question about the possible and effective kidney transplantation in elderly patients with various severe comorbidities. The analysis is based on an example of successful kidney transplantation from a deceased donor when a 67-year-old patient had severe concomitant background: obesity, diabetes mellitus, and cardiovascular disturbances. Despite unfavorable prognosis and further unpredictable illnesses such as COVID-19, candidal esophagitis, coronary attack, and pneumonia, the patient has not develop graft injury or rejection and kept sufficient kidney function. The research was mainly focused on coexisting illnesses and their influence on kidney transplantation consequences. Following disease groups were discussed regarding their impact on transplantation outcomes and prognosis: arterial hypertension, cardiac disorders, diabetes mellitus, and obesity. Patient’s age, previous interventions, and  comorbidities were observed for association with outcomes and risk of graft rejection. A review of available publications compared approaches for recipient selection in different clinical centers as well.

The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, which will help identify critical personal characteristics based on conditioned space analysis. The condition space is given in cube form as a reflection of the functional relationship of the general parameters to the studied object. The dataset consists of 51 instances, and ten parameters are processed using different clustering and regression models. Days in hospital is the target variable. A condition space cube is formed based on clustering analysis and features selection. In this manner, a hierarchical predictor based on clustering and an ensemble of weak regressors is built. The quality of the developed hierarchical predictor for Root Mean Squared Error metric is 1.47 times better than the best weak predictor (perceptron with 12 units in a single hidden layer).