A comparison of machine learning models to classify muscle biopsies based on gene expression data. Accuracy and 95% CI in the 1000 test sets of the different machine learning models to classify muscle biopsies into normal muscle tissue (NT), dermatomyositis (DM), antisynthetase syndrome (AS), inclusion body myositis (IBM) or immune-mediated necrotising myopathy (IMNM).
| NT | DM | AS | IBM | IMNM | |
| Linear SVM | 94.7 (87.2 to 100.0) | 92.0 (85.1 to 97.9) | 91.0 (85.1 to 95.7) | 95.0 (91.5 to 100.0) | 92.0 (85.1 to 97.9) |
| AdaBoost | 91.5 (83.0 to 97.9) | 89.6 (80.9 to 95.7) | 89.1 (83.0 to 93.6) | 91.9 (80.9 to 97.9) | 85.8 (76.6 to 93.6) |
| Gaussian process | 94.2 (87.2 to 100.0) | 82.9 (74.5 to 91.5) | 87.2 (80.9 to 91.5) | 91.0 (85.1 to 95.7) | 79.6 (68.1 to 89.4) |
| Nearest neighbours | 91.5 (85.1 to 97.9) | 87.8 (80.9 to 95.7) | 87.2 (83.0 to 89.4) | 90.6 (89.4 to 93.6) | 77.4 (66.0 to 87.2) |
| Random forest | 89.7 (83.0 to 95.7) | 85.6 (76.6 to 93.6) | 85.7 (78.7 to 91.5) | 90.4 (87.2 to 93.6) | 78.3 (68.1 to 87.2) |
| Neural network | 89.1 (72.3 to 97.9) | 83.5 (44.7 to 95.7) | 87.4 (74.4 to 93.6) | 91.1 (89.4 to 97.9) | 71.6 (36.2 to 95.7) |
| Decision tree | 87.8 (76.6 to 95.7) | 86.5 (76.6 to 93.6) | 85.0 (74.5 to 91.5) | 85.7 (76.6 to 93.6) | 76.1 (57.4 to 89.4) |
| RBF SVM | 85.1 (85.1 to 85.1) | 82.6 (76.6 to 87.2) | 87.2 (87.2 to 87.2) | 89.4 (89.4 to 89.4) | 64.0 (63.8 to 66.0) |
| Gaussian Naïve Bayes | 85.1 (85.1 to 85.1) | 80.2 (70.2 to 89.4) | 86.4 (83.0 to 89.4) | 89.3 (87.2 to 91.5) | 66.1 (53.2 to 78.7) |
| QDA | 86.5 (78.7 to 93.6) | 63.5 (48.9 to 76.6) | 75.5 (61.7 to 87.2) | 80.4 (68.1 to 89.4) | 63.1 (46.8 to 76.6) |
The models are sorted based on the average accuracy of all the groups.
AdaBoost, adaptative boosting; QDA, quadratic discriminant analysis; RBF, radial basis function; SVM, support vector machines.