Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. The K t / V value is the gold standard of hemodialysis adequacy. However, K t / V requires repeated blood drawing and evaluation; it is hard to monitor dialysis adequacy frequently. In order to meet the need for repeated clinical assessments of dialysis adequacy, we want to find a noninvasive way to assess dialysis adequacy. Therefore, we collect some clinically relevant data and develop a machine learning- (ML-) based model to predict dialysis adequacy for clinical hemodialysis patients. We collect 250 patients, including gender, age, ultrafiltration (UF), predialysis body weight (preBW), postdialysis body weights (postBW), blood pressure (BP), heart rate (HR), and blood flow (BF). An efficient graph-based Takagi-Sugeno-Kang Fuzzy System (G-TSK-FS) model is proposed to predict the dialysis adequacy of hemodialysis patients. The root mean square error (RMSE) of our model is 0.1578. The proposed model can be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice. Our G-TSK-FS model could be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice.

Traffic congestion is a complex problem affected by many factors. Considering the advantage of extension theory which abstracting complex problems in dealing with conflict events, this paper combines with extension theory and traffic congestion, and constructs the early-warning model of traffic congestion including 9 indexes and 5 levels based on the factors of traffic flow, road load, intersection capacity, weather condition, emergency and so on. In view of the conflict interaction of various factors, this paper uses the simple extension correlation function to determine the evaluation weights. The example calculation shows that the model is suitable for evaluation of urban traffic congestion qualitatively, and for assisting early-warning decision quantitatively.