Table 4.
Predictive performance of the best ML models using NIR spectral data from second trimester serum samples.
| Range a | Pretreatment | Sp | Se | NER | |||
|---|---|---|---|---|---|---|---|
| Av | StD | Av | StD | Av | StD | ||
| Full | 2D (W = 15) + N + MC | 0.8133 | 0.0324 | 0.1150 | 0.0556 | 0.4642 | 0.0321 |
| R1 | WLS + N + MC | 0.8754 | 0.0414 | 0.1625 | 0.1218 | 0.5189 | 0.0643 |
| R2 | 2D (W = 3) + N + MC | 0.6821 | 0.0288 | 0.3875 | 0.1191 | 0.5348 | 0.0613 |
| R3 | 1D (W = 15) + MC | 0.8713 | 0.0361 | 0.7075 | 0.0783 | 0.7894 | 0.0431 |
a Full: 10,500–4000 cm−1; R1: 10,500–7600 cm−1; R2: 7600–5100 cm−1; R3: 5100–4000 cm−1. Sp: specificity; Se: sensitivity; NER: non-error rate; Av: average; StD: standard deviation; R1: Range 1; R2: Range 2; R3: Range 3; 2D: second derivative; W: width; N: normalization; MC: mean centering; WLS: weighted least squares; 1D: first derivative.