Table 2 Results of different methods, where “CNN” indicates the performance of the proposed model.
From: Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Method | Training set | Testing set | ||||||
|---|---|---|---|---|---|---|---|---|
Precision | Recall | F1-score | Accuracy | Precision | Recall | F1-score | Accuracy | |
SVM | 0.96 | 0.95 | 0.96 | 0.9549 | 0.93 | 0.93 | 0.93 | 0.9315 |
MultinomialNB | 0.93 | 0.92 | 0.92 | 0.9236 | 0.87 | 0.86 | 0.86 | 0.8600 |
LogisticRegression | 0.93 | 0.93 | 0.93 | 0.9293 | 0.92 | 0.92 | 0.92 | 0.9175 |
KNeighborsClassifier | 0.89 | 0.89 | 0.89 | 0.8911 | 0.90 | 0.89 | 0.89 | 0.8925 |
CNN | 0.9947 | 0.9946 | 0.9946 | 0.9982 | 0.9594 | 0.9602 | 0.9596 | 0.9867 |
