Table 1:
Response models and their respective metrics for the classification of health status and location variables. The column Class includes Health Status (Cancer, Healthy), Quartile (1, 2, 3, 4), Centile (50, 75, 95, and 100%), and BIVA status (11, 12, 13, 14, 21, 22, 23, 31, 32, 33, 41, 42, 43, 44).
| Response | Model | Accuracy | Class | Precision | Recall | F1-score |
|---|---|---|---|---|---|---|
| Health status | Fine Tree | 92.80% | Cancer | 0.912 | 0.951 | 0.931 |
| Healthy | 0.947 | 0.905 | 0.926 | |||
| BIVA status | Fine Tree | 99.50% | 11 | 1.000 | 1.000 | 1.000 |
| 12 | 1.000 | 1.000 | 1.000 | |||
| 13 | 1.000 | 1.000 | 1.000 | |||
| 14 | 1.000 | 1.000 | 1.000 | |||
| 21 | 1.000 | 1.000 | 1.000 | |||
| 22 | 1.000 | 1.000 | 1.000 | |||
| 23 | 0.857 | 0.750 | 0.800 | |||
| 31 | 1.000 | 1.000 | 1.000 | |||
| 32 | 1.000 | 1.000 | 1.000 | |||
| 33 | 0.889 | 0.941 | 0.914 | |||
| 41 | 1.000 | 1.000 | 1.000 | |||
| 42 | 1.000 | 1.000 | 1.000 | |||
| 43 | 1.000 | 1.000 | 1.000 | |||
| 44 | 1.000 | 1.000 | 1.000 | |||
| Quartile | Linear SVM | 100% | 1 | 1.000 | 1.000 | 1.000 |
| 2 | 1.000 | 0.983 | 0.991 | |||
| 3 | 0.990 | 1.000 | 0.995 | |||
| 4 | 1.000 | 1.000 | 1.000 | |||
| Centile | RUS Boosted Tree | 97% | 50% | 0.988 | 0.984 | 0.986 |
| 75% | 0.959 | 0.953 | 0.956 | |||
| 95% | 0.948 | 0.938 | 0.943 | |||
| 100% | 0.957 | 1.000 | 0.978 |
Fig. 1:
Confusion matrix of trained models: a) Health status, b) BIVA status, c) quartile and d) centile responses.
Fig. 2:
Feature importance of trained models: a) Health status, b) BIVA Status, c) quartile and d) centile responses.
Table 2:
Accuracy parameters of each model.
| Response | Model | R2 | RMSE | MSE | MAE |
|---|---|---|---|---|---|
| Zc (Ω) | Linear | 1.000 | 0.351(Ω2) | 0.123 (Ω) | 0.233 (Ω) |
| θc (°) | Linear | 0.980 | 0.237 (°2) | 0.056 (°) | 0.166 (°) |
| Xcc (Ω) | Linear SVM | 0.990 | 2.378 (Ω2) | 5.652 (Ω) | 1.674 (Ω) |
| Rc (Ω) | Linear SVM | 1.000 | 4.216 (Ω2) | 17.773 (Ω) | 3.223 (Ω) |
Fig. 3:
Response vs predicted plot of the selected responses.
Fig. 4:
observable and predictions of Zc, θc, Xcc and Rc across various categories: Health Status (Cancer, Healthy), Sex, Quartile (1, 2, 3, 4), Centile (50, 75,95 and 100%), and BIVA status (11, 12, 13, 14, 21, 22, 23, 31, 32, 33, 41, 42, 43, 44).
Fig. 5:
Schematic representation of BIA vector analysis (BIVA), presenting the maximum and minimum values of Zc, θc, Xcc and Rc (relative to their respective medians).