Performance of the decision stump classifier | ||||||
Evaluation method | Sensitivity | Accuracy | Specificity | AUC | PPVa | NPVa |
10-fold cross validation | 67.2 % | 73.4 % | 74.9 % | 0.710 | 38.1 % | 91.0 % |
testing on each patient’s last assessment | 51.1 % | 73.9 % | 82.0 % | 0.665 | 50.0 % | 82.6 % |
Performance of the six advanced classifiers measured by the 10-fold cross validation method | ||||||
Classifier | Sensitivity | Accuracy | Specificity | AUC | PPVa | NPVa |
Multiboost with decision stumps | 73.8 % | 71.8 % | 71.4 % | 0.761 | 37.1 % | 92.4 % |
Support vector machine | 71.5 % | 72.0 % | 72.0 % | 0.718 | 37.0 % | 91.8 % |
Deep learning | 71.6 % | 72.3 % | 72.5 % | 0.744 | 37.2 % | 91.8 % |
Naive Bayes | 59.8 % | 78.1 % | 82.3 % | 0.777 | 43.7 % | 90.0 % |
k-nearest neighbor | 56.9 % | 73.3 % | 77.0 % | 0.704 | 36.0 % | 88.7 % |
Random forest | 48.3 % | 75.8 % | 82.0 % | 0.662 | 37.9 % | 87.5 % |
Performance of the six advanced classifiers measured by the method of testing on each patient’s last assessment | ||||||
Classifier | Sensitivity | Accuracy | Specificity | AUC | PPVa | NPVa |
Multiboost with decision stumps | 74.5 % | 74.4 % | 74.4 % | 0.757 | 50.7 % | 89.2 % |
Support vector machine | 70.2 % | 73.3 % | 74.4 % | 0.723 | 49.3 % | 87.6 % |
Deep learning | 68.1 % | 72.2 % | 73.7 % | 0.738 | 47.8 % | 86.7 % |
Naive Bayes | 44.7 % | 73.9 % | 84.2 % | 0.783 | 50.0 % | 81.2 % |
k-nearest neighbor | 48.9 % | 73.9 % | 82.7 % | 0.773 | 50.0 % | 82.1 % |
Random forest | 38.3 % | 75.6 % | 88.7 % | 0.678 | 54.5 % | 80.3 % |