Table 1.
Machine learning methodology | Samples | Model | Number of variables | % Accuracy score | Model validation | Model evaluation | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TPR | TNR | TP | FP | TN | FN | TPR | TNR | TP | FP | TN | FN | AUC | |||||
randomForest (RF) | All samples (1st cohort) | Four metabolomes | 54 | 73 | 0.87 | 0.55 | 87 | 45 | 55 | 13 | 0.58 | 0.64 | 58 | 36 | 64 | 42 | 0.72 |
randomForest (RF) | All samples (1st cohort) | DHA metabolome | 23 | 81 | 0.9 | 0.68 | 90 | 32 | 68 | 10 | 0.39 | 0.55 | 39 | 45 | 55 | 61 | 0.44 |
randomForest (RF) | All samples (1st cohort) | n-3 DPA metabolome | 10 | 69 | 0.83 | 0.5 | 83 | 50 | 50 | 17 | 0.78 | 0.32 | 78 | 68 | 32 | 22 | 0.58 |
randomForest (RF) | All samples (1st cohort) | EPA metabolome | 3 | 65 | 0.8 | 0.45 | 80 | 55 | 45 | 20 | 0.53 | 0.32 | 53 | 68 | 32 | 47 | 0.6 |
randomForest (RF) | All samples (1st cohort) | AA metabolome | 18 | 65 | 0.77 | 0.5 | 77 | 50 | 50 | 23 | 0.83 | 0.77 | 83 | 23 | 77 | 17 | 0.89 |
randomForest (RF) | All samples (1st cohort) | Clin. Score | 11 | 50 | 0.6 | 0.36 | 60 | 64 | 36 | 40 | 0.03 | 0.88 | 3 | 12 | 88 | 97 | 0.53 |
randomForest (RF) | All samples (2nd cohort) | RvD4, 10S, 17S-diHDPA, 15R-LXA4, MaR1n-3 DPA | 4 | 83 | 0.92 | 0.68 | 92 | 32 | 68 | 8 | 0.83 | 0.59 | 83 | 41 | 59 | 17 | 0.8 |
randomForest (RF) | All samples (2nd cohort) | RvD4, 10S, 17S-diHDPA, 15R-LXA4, 5S,12S-diHETE, 4S,14S-diHDHA, MaR1n-3 DPA | 6 | 86 | 0.94 | 0.73 | 94 | 27 | 73 | 6 | 0.87 | 0.64 | 87 | 36 | 64 | 13 | 0.79 |
randomForest (RF) | Fibroid samples (1st & 2nd cohort) | RvD4, 10S, 17S-diHDPA, 15R-LXA4, MaR1n-3 DPA | 4 | 88 | 0.89 | 0.87 | 89 | 13 | 87 | 11 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
randomForest (RF) | Lymphoid samples (1st & 2nd cohort) | RvD4, 10S, 17S-diHDPA, 15R-LXA4, MaR1n-3 DPA | 4 | 83 | 0.89 | 0.7 | 89 | 30 | 70 | 11 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
randomForest (RF) | Myeloid samples (1st & 2nd cohort) | RvD4, 10S, 17S-diHDPA, 15R-LXA4, MaR1n-3 DPA | 4 | 70 | 0.86 | 0.47 | 86 | 53 | 47 | 14 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
Classyfire (SVM) | All samples (1st cohort) | Four metabolomes | 54 | 61 | 0.63 | 0.54 | 63 | 46 | 54 | 37 | 0.94 | 0.05 | 94 | 95 | 5 | 6 | 0.53 |
Classyfire (SVM) | All samples (1st cohort) | DHA metabolome | 23 | 62 | 0.65 | 0.56 | 65 | 44 | 56 | 35 | 0.78 | 0.18 | 78 | 82 | 18 | 22 | 0.54 |
Classyfire (SVM) | All samples (1st cohort) | n-3 DPA metabolome | 10 | 61 | 0.63 | 0.54 | 63 | 46 | 54 | 37 | 0.94 | 0.23 | 94 | 77 | 23 | 6 | 0.66 |
Classyfire (SVM) | All samples (1st cohort) | EPA metabolome | 3 | 60 | 0.62 | 0.52 | 62 | 48 | 52 | 38 | 0.92 | 0.05 | 92 | 95 | 5 | 8 | 0.58 |
Classyfire (SVM) | All samples (1st cohort) | AA metabolome | 18 | 58 | 0.6 | 0.48 | 60 | 52 | 48 | 40 | 0.92 | 0.09 | 92 | 91 | 9 | 8 | 0.66 |
TPR sensitivity, TNR specificity, TP true positives, FP false positives, TN true negatives, FN false negatives, AUC area under the curve.