Objective: As a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWdata were collected when the dry weight was measured using the BIS (DW). The gap between the two(Gap) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg. Methods: Based on the gap between DW and DW, 972, 303, and 384 patients were placed in groups withgaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seenthat the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machinelearning. As Gap increases, it is more difficult to predict the target property. As Gap increase, the meanvalues of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue indextended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellularwater ratio tended to increase. Results: Machine learning made it slightly easier to predict DW based on DW under limitedconditions and gave better insights into predicting DW. Malnutrition-related factors and ECW were importantin reflecting the differences between DW and DW. Conclusions: Objective: Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and thesurvival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dryweight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study,we tried to predict the clinically proper dry weight (DW) using machine learning for patient’s clinicalinformation including BIS. We then analyze the factors that influence the prediction of the clinical dry weight. Methods: As a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWdata were collected when the dry weight was measured using the BIS (DW). The gap between the two(Gap) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg. Results: Based on the gap between DW and DW, 972, 303, and 384 patients were placed in groups withgaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seenthat the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machinelearning. As Gap increases, it is more difficult to predict the target property. As Gap increase, the meanvalues of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue indextended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellularwater ratio tended to increase. Conclusions: Machine learning made it slightly easier to predict DW based on DW under limitedconditions and gave better insights into predicting DW. Malnutrition-related factors and ECW were importantin reflecting the differences between DW and DW.