Symptoms Based Disease Prediction from Bengali Text Using Transformer Network Based Pretrained Model

Recently, automated methods for disease identification have gained popularity. Many research studies use different languages for disease detection systems. We describe a disease identification method using our own developed Bengali symptoms-based disease prediction dataset that is written in the Bengali language. We have designed a disease prediction system using a transfer learning technique where we use a transformer network-based pertained model called BERT (Bidirectional Encoder Representations from Transformers). We have used the Hugging Face Transformer and then further fine-tune the model on our relatively smaller dataset. These transformer network-based deep learning techniques help us to achieve a satisfactory accuracy of 93.75%, which is good enough to identify most of the diseases using our Bengali disease dataset. The aim of the research is to use Bangla medical text data and a transfer-network based pertained model to accurately identify relevant diseases from symptoms. This will allow patients to treat their disease instantly and ensure effective disease prediction.