Table 2.
Summary of the Logistic Regression and Classification and Regression Tree (CART) Models in the Prediction of OSA
| Number of Photographic Variables | Number of Clinical Variables | Overall Correct Classification | ROC AUC | |
|---|---|---|---|---|
| Logistic Regression Model 1 – Calibrated Photographic Measurements | 4 | - | 76.1% | 0.82 |
| Logistic Regression Model 2 – Uncalibrated Photographic Measurements† | 4 | - | 71.1% | 0.80 |
| Logistic Regression Model 3 – Clinical Measurements | - | 3 (Age, BMI, WA) | 76.1% | 0.78 |
| Logistic Regression Model 4 – Photographic and Clinical Measurements | 3 | 2 (WA, MMC) | 78.3% | 0.87 |
| CART Model 1 – Single Photographic Measurement | 1 | - | 64.4% | 0.68 |
| CART Model 2 – Multiple Photographic Measurements | 4 | - | 76.7% | 0.84 |
†
model built using measurements that do not require calibration (e.g., ratios and angular measurements); ROC AUC = receiver operating characteristic, area under the curve; BMI = body mass index; WA = witnessed apneas; MMC = modified Mallampati class