From: Machine learning approach for the detection of vitamin D level: a comparative study
Predictors | \({\chi }^{2}\)(df)a | P value | Predictors | \({\chi }^{2}\)(df)a | P value |
---|---|---|---|---|---|
Gender | 1.622(2) | 0.444 | TRY | 57.296 (2) |  < 0.001 |
Age groups | 27.743 (10) | 0.002 | MtS | 70.231 (2) |  < 0.001 |
SBP | 2.630 (2) | 0.268 | Having alcohol | 5.501(2) | 0.064 |
DBP | 3.485 (2) | 0.175 | Having smoke | 3.796 (2) | 0.150 |
FBS | 2.516 (2) | 0.284 | Skin Tone | 60.897 (2) |  < 0.001 |
HOMA-IR | 0.666 (2) | 0.717 | Usage of sun protection cream | 4.124 (2) | 0.127 |
hs-CRP | 28.509 (2) |  < 0.001 | Sunlight exposure status | 14.094(2) |  < 0.001 |
UAL | 12.405 (2) | 0.002 | Usage of MMS | 31.727(2) |  < 0.001 |
HDL | 42.633 (2) |  < 0.001 | Usage of FOS | 26.390 (2) |  < 0.001 |
TCL | 2.907(2) | 0.234 | WC | 45.587 (2) |  < 0.001 |
LDL | 1.786 (2) | 0.409 | BMI | 57.004(2) |  < 0.001 |