From: Machine learning algorithm for early detection of end-stage renal disease
Subgroup size | Positive cases | C- statistics | Sensitivity | Specificity | PPV | NPV | |
---|---|---|---|---|---|---|---|
Males ckd S3/S3. Age 60+ | 1784 | 164 | 0.919 | 0.756 | 0.931 | 0.528 | 0.974 |
Males,ckd S3/S4, Age 60- | 348 | 44 | 0.878 | 0.659 | 0.908 | 0.509 | 0.948 |
Males ckd S1/S2 Age 60+ | 1061 | 16 | 0.925 | 0.625 | 0.983 | 0.357 | 0.995 |
Males ckd S1/S2, Age 60- | 559 | 5 | 0.968 | 0.600 | 0.982 | 0.231 | 0.996 |
Females ckd S3/S4 Age 60+ | 1862 | 152 | 0.918 | 0.711 | 0.944 | 0.529 | 0.973 |
Females ckd S3/S4 Age 60- | 230 | 34 | 0.891 | 0.765 | 0.913 | 0.605 | 0.957 |
Females ckd S1/S2 Age 60+ | 1064 | 13 | 0.906 | 0.765 | 0.991 | 0.526 | 0.997 |
Females ckd S1/S2 Age 60- | 426 | 10 | 0.918 | 0.300 | 0.993 | 0.500 | 0.983 |