Table 2 Characteristics of patients and dialysis parameters in the training and test data
From: Predicting dry weight change in Hemodialysis patients using machine learning
|  | All (n = 314) | Train data (n = 237) | Test data (n = 77) | ||||
|---|---|---|---|---|---|---|---|
Demographics | Â | Â | Â | Â | Â | Â | |
Age, years | 66.4 | ± 12.4 | 66.4 | ± 12.5 | 66.3 | ± 12.3 | |
Gender (Male/Female) | 224 | /90 | 170 | /67 | 54 | /23 | |
Race, n (%) | Â | Â | Â | Â | Â | Â | |
Asian (Japanese) | 312 | (99.4) | 235 | (99.2) | 77 | (100.0) | |
Asian (Southeast Asian) | 2 | (0.6) | 2 | (0.8) | 0 | (0.0) | |
Dialysis vintage, years | 4.0 | (2.0–8.0) | 4.0 | (2.0-8.8) | 4.0 | (2.0–6.0) | |
Body mass index, kg/m2 | 22.2 | ± 4.3 | 22.1 | ± 3.8 | 22.6 | ± 5.4 | |
Primary disease, n (%) | Â | Â | Â | Â | Â | Â | |
Diabetic nephropathy | 133 | (42.4) | 95 | (40.1) | 38 | (49.4) | |
Chronic glomerulonephritis | 61 | (19.4) | 51 | (21.5) | 10 | (13.0) | |
Nephrosclerosis | 52 | (16.6) | 40 | (16.9) | 12 | (15.6) | |
Polycystic kidney disease | 12 | (3.8) | 11 | (4.6) | 1 | (1.3) | |
Others | 56 | (17.8) | 40 | (16.9) | 16 | (20.8) | |
Dialysis parameter | Â | Â | Â | Â | Â | Â | |
UF (L/session) | 2.4 | ± 0.97 | 2.4 | ± 0.95 | 2.6 | ± 1.03 | |
UFR (L/hr) | 0.61 | ± 0.24 | 0.60 | ± 0.24 | 0.64 | ± 0.25 | |
Dry Weight (kg) | 59.7 | ± 14.2 | 59.3 | ± 12.8 | 60.8 | ± 17.6 | |
Dialysis mode, n (%) | Â | Â | Â | Â | Â | Â | |
HD | 5047 | (7.3) | 3238 | (6.2) | 1809 | (10.4) | |
HD + ECUM | 306 | (0.4) | 146 | (0.3) | 160 | (0.9) | |
OHDF | 63,700 | (91.8) | 48,320 | (93.0) | 15,380 | (88.2) | |
OHDF + ECUM | 254 | (0.4) | 185 | (0.4) | 69 | (0.4) | |
ECUM | 68 | (0.1) | 46 | (0.1) | 22 | (0.1) | |