From: Machine learning for predicting diabetes risk in western China adults
Category | Features | Multivariate logistic regression analysis | ||
---|---|---|---|---|
Beta | OR(95%CI) | P value | ||
Questionnaire | Sex, n(%) | |||
 Male |  |  |  | |
 Female | 0.1095 | 1.116 (1.103 1.129) |  < 0.001 | |
 Age(year) | 0.0299 | 1.03 (1.030 1.031) |  < 0.001 | |
Ethnicity, n(%) | ||||
 Uyghur |  |  |  | |
 Han | 0.384 | 1.469 (1.452 1.485) |  < 0.001 | |
 Kazak | 0.549 | 0.578 (0.564 0.592) |  < 0.001 | |
 Hui | 0.465 | 1.592 (1.56 1.624) |  < 0.001 | |
 Khalkha | 0.732 | 0.481 (0.445 0.52) |  < 0.001 | |
 Mongol | 0.524 | 0.592 (0.554 0.632) |  < 0.001 | |
 Tajik | 1.09 | 0.335 (0.264 0.419) |  < 0.001 | |
 Other | 0.142 | 1.153 (1.094 1.215) |  < 0.001 | |
EH, n(%) | ||||
 Balanced diet |  |  |  | |
 Meat based | 0.0762 | 1.079 (1.037 1.123) |  < 0.001 | |
 Vegetarian based | 0.018 | 1.018 (0.985 1.052) | 0.282349 | |
SS, n(%) | ||||
 Never smoked |  |  |  | |
 smoking | 0.0645 | 1.067 (1.048 1.086) |  < 0.001 | |
 Quit smoking | 0.171 | 1.186 (1.131 1.244) |  < 0.001 | |
HTN, n(%) | ||||
 No |  |  |  | |
 Yes | 1.212 | 3.361 (3.324 3.398) |  < 0.001 | |
CAD, n(%) | ||||
 No |  |  |  | |
 Yes | 0.471 | 1.603 (1.579 1.627) |  < 0.001 | |
PDM, n(%) | ||||
 No |  |  |  | |
 Yes | 1.310 | 3.706 (3.575 3.841) |  < 0.001 | |
Routine examination | WC (cm) | 0.0121 | 1.012 (1.012 1.013) |  < 0.001 |
BMI (kg/m2) | 0.0147 | 1.015 (1.013 1.017) |  < 0.001 | |
Laboratory test | HGB, g/L | 0.001 | 0.999 (0.999 0.999) |  < 0.001 |
WBC, × 109/L | 0.126 | 1.134 (1.13 1.138) |  < 0.001 | |
PLT, × 109/L | 0.002 | 0.998 (0.998 0.998) |  < 0.001 | |
FBG, mmol/L | 0.946 | 2.574 (2.556 2.593) |  < 0.001 | |
ECG, n(%) | ||||
 TC, mmol/L | 0.005 | 1.005 (0.999 1.012) | 0.090 | |
 TG, mmol/L | 0.255 | 1.29 (1.278 1.303) |  < 0.001 | |
 LDLC, mmol/L | 0.019 | 0.981 (0.974 0.988) |  < 0.001 | |
 HDLC, mmol/L | 0.196 | 0.822 (0.810 0.833) |  < 0.001 |