Deep Learning Based Framework for Multi-disease Detection Using CNN-BiLSTM

There has been a surge in interest in developing accurate and efficient illness prediction models to help in early diagnosis and treatment planning in recent years. Despite innovations in deep learning techniques, there is enormous potential for leveraging these techniques for illness prediction across different domains. This study presents a deep learning-based technique for predicting multiple diseases, like diabetes, liver, and Kidney, to improve diagnosis accuracy and facilitate prompt treatments. In this paper, the authors employ two deep learning algorithms, CNN, LSTM, and proposed CNN-BILSTM, to determine if patients are at risk of illness. The suggested CNN-BiLSTM approaches outperform others for all three diseases, representing a major improvement, especially for a disease that impacts a large population.