From: Machine learning approach for the detection of vitamin D level: a comparative study
 | Actual | Predicted | Recall | Error Rate | ||
---|---|---|---|---|---|---|
 | Deficient | Inadequate | Adequate | |||
SVM | Deficient | 0.86 | 0.06 | 0.08 | 0.86 | 0.20 |
Inadequate | 0.11 | 0.69 | 0.20 | 0.69 | ||
Adequate | 0.17 | 0.09 | 0.74 | 0.74 | ||
 | Precision | 0.90 | 0.77 | 0.55 |  | |
OLR | Deficient | 0.80 | 0.08 | 0.12 | 0.80 | 0.28 |
Inadequate | 0.17 | 0.60 | 0.23 | 0.60 | ||
Adequate | 0.13 | 0.26 | 0.61 | 0.61 | ||
Precision | 0.88 | 0.62 | 0.44 | Â | ||
ENOR | Deficient | 0.94 | 0.02 | 0.04 | 0.94 | 0.08 |
Inadequate | 0.03 | 0.91 | 0.06 | 0.91 | ||
Adequate | 0.04 | 0.09 | 0.87 | 0.87 | ||
Precision | 0.98 | 0.89 | 0.80 | Â | ||
RF | Deficient | 0.95 | 0.02 | 0.03 | 0.95 | Â |
 | Inadequate | 0.09 | 0.91 | 0.00 | 0.91 | 0.06 |
 | Adequate | 0.00 | 0.04 | 0.96 | 0.96 |  |
 | Precision | 0.97 | 0.91 | 0.92 |  |  |