An automated deep learning model combining 1-D CorrNN and bidirectional LSTM for salivary analysis achieved an average accuracy rate of 98.08% for detecting Chronic Kidney Disease.
Does a deep learning model combining 1-D CorrNN and bidirectional LSTM improve the accuracy of automated CKD detection from human saliva samples?
A deep learning model combining 1-D CorrNN and bidirectional LSTM achieved 98.08% accuracy in detecting CKD from human saliva samples.
In this paper, we aim to explore the feasibility of salivary analysis for Chronic Kidney Disease (CKD) detection and thereby design an automated mechanism to detect CKD through analysis of human saliva samples. We have implemented an improved deep learning model that combines both a one-dimensional Correlational Neural Network (1-D CorrNN) and bidirectional Long Short-Term Memory (LSTM) network for making accurate predictions. The LSTM network is integrated with the neural model to utilize the capabilities of both these networks to analyze the time-series data. The proposed model is trained and tested with a CKD sensing module. The application of deep learning algorithms helps to improve the detection accuracy as they are capable of discovering the best features from the input data. The proposed method achieved an average accuracy rate of 98.08% for the testing dataset. The results show that the proposed detection module and classification algorithm substantially advance the current methodologies, and provides more accurate predictions compared to conventional methods.
Bhaskar et al. (Mon,) conducted a other in Chronic Kidney Disease (CKD). 1-D CorrNN and bidirectional LSTM model for salivary analysis vs. Conventional methods was evaluated on Detection accuracy. An automated deep learning model combining 1-D CorrNN and bidirectional LSTM for salivary analysis achieved an average accuracy rate of 98.08% for detecting Chronic Kidney Disease.