Outdoor patient classification in hospitals based on symptoms in Bengali language

Figures & data

Table 1. Number of data in Dataset-1 and Dataset-2 (train and test split).

Dataset	Total data	Train set	Test set
Dataset-1 (Bengali Colloquial Dataset)	704	563	141
Dataset-2 (Bengali Colloquial Dataset + Translated data)	7041	5633	1408

Table 2. Sample records from the Dataset.

Table 3. Vector representation of the sentences.

Figure 1. Class labels of the combined dataset.

Figure 2. Basic abstract of the methodology.

Figure 3. Convolutional neural network architecture.

Table 4. Test performance result (Dataset 1).

Classifier	Accuracy	Precision	Recall	F1 Score
Random Forest	0.7801	0.79	0.78	0.78
SVM	0.7733	0.78	0.77	0.77
Logistic Regression	0.7689	0.77	0.76	0.765

Table 5. Test performance result (Dataset 2).

Classifier	Accuracy	Precision	Recall	F1 Score
Random forest	0.9766	0.98	0.98	0.98
SVM	0.9759	0.97	0.98	0.97
Logistic regression	0.9759	0.98	0.98	0.98

Table 6. Deep-learning model performance after 30 epochs (Dataset 1).

Model	Train accuracy	Test accuracy
LSTM	97.69	80.14
GRU	97.51	76.60
CNN	99.29	82.27
CNN-LSTM	99.29	78.01

Figure 4. Training and testing loss values (first column) and accuracy values (second coloumn) using different deep-learning models using Dataset 1 during 30 epochs.

Figure 5. Training and testing loss values (first column) and accuracy values (second coloumn) using different deep-learning models using Dataset 2 during 30 epochs.

Table 7. Deep-learning model performance with 30 epochs (Dataset 2).

Model	Train accuracy	Test accuracy
LSTM	99.68	96.88
GRU	99.63	96.45
CNN	99.73	97.37
CNN-LSTM	99.73	96.84

Table 8. Comparison of model predictions on Dataset 1.

Approach	Prediction of models
Random forest	94.60
SVM	93.31
Logistic regression	90.10
LSTM	93.32
GRU	92.33
CNN	94.17
CNN-LSTM	91.47

Table 9. Overall classification report of the deep-learning models.

Type	Precision	Recall	F1 score	ROC AUC
LSTM	0.935	0.934	0.933	0.955
GRU	0.924	0.923	0.923	0.949
CNN	0.946	0.942	0.943	0.962
CNN-LSTM	0.934	0.914	0.921	0.948

Figure 6. Graphical representation of prediction of models.

Figure 7. Confusion matrix on RF model prediction.

Table 10. Prediction table for specialist with correct classification.

Table 11. Prediction table for specialist with incorrect classification.