Research Article
Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention
Table 15
Performance comparison of accuracy (%) of the proposed deep learning models with other deep learning models on the same dataset.
| Methods | Hall mark dataset | AIM dataset |
| CNN | 68.55 | 82.17 | LSTM | 70.76 | 83.16 | BiLSTM | 72.58 | 87.77 | CNN-LSTM | 71.81 | 91.98 | CNN-BiLSTM | 73.99 | 93.06 | Logistic regression | 61.91 | 72.92 | NBC | 65.35 | 73.84 | SVM | 66.99 | 84.55 | BiGRU | 69.34 | 89.98 | Proposed method 1: quad channel hybrid LSTM model | 75.98 | 96.72 | Proposed method 2: hybrid BiGRU with multihead attention model | 74.71 | 95.76 |
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