Introduction: Inadequate dialysis for patients' kidneys as a mortality risk necessitates the presence of a pattern to assist staff in dialysate part to provide the proper services for dialysis patients and also the proper management of their treatment. Since the role of buffer type in the adequacy of dialysis is determinative, the present study is aimed at determining hemodialysis buffer type.

Methods: Cross-sectional method was applied in the present study. The population included the data extracted from Ali Ibn Abi Talib hospitals in Zahedan from May-June 2016. In this study Clementine 12.0 has been used for data analysis. In the present study Decision trees C5.0, Classification and Regression Tree, Chi-Squared Automatic Interaction Detector, Unbiased and Efficient Statistical Tree and Neural Network algorithms were executed.

Results: The obtained accuracy for executing decision trees C5.0, Classification and Regression Tree, Chi-Squared Automatic Interaction Detector, Unbiased and Efficient Statistical Tree and Neural Network equals 0.9263, 0.9047, 0.8872, 0.8720 and 0.8754, respectively. The results of indices including sensitivity, specificity, accuracy, precision, NPV, FM, GM, FPR, FNR, FDR, ER for C5.0 decision tree are indicators of better performance of this algorithm compared to the other algorithms.

Conclusion: The extracted rules for a new sample having specific features can predict proper dialysis buffer. Hence, the proposed model helps us in predicting more precise hemodialysis buffer type and also the proper management of patient treatment which result in better performance among health organization.