In this article, we propose a new sensing technique for the automated detection of kidney disease. The salivary urea concentration is monitored to detect the disease. A new sensing approach is introduced to monitor the urea levels in the saliva sample. Further, for analyzing the raw signals obtained from the sensor, we have implemented a 1-Dimensional (1-D) deep learning Convolutional Neural Network (CNN) algorithm which is incorporated with a Support Vector Machine (SVM) classifier. The use of CNN-SVM integrated network enhanced the classification accuracy of the model. The proposed model successfully classified the samples with an accuracy of 98.04%.

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.

In this paper, we have proposed an automated medical system for detecting type 2 diabetes from exhaled breath. Human breath can be used as a diagnostic sample for detecting many diseases as it contains many gases that are dissolved in the blood. Breath-based analysis stands out among the different non-invasive ways of detection as it provides more accurate predictions and offers many advantages. In this work, the concentration of acetone in the exhaled breath is analysed to detect type 2 diabetes. A new sensing module consisting of an array of sensors is implemented for monitoring the acetone concentration to detect the disease. Deep learning algorithms like Convolutional Neural Networks (CNN) are normally used to automatically analyse medical data to make predictions. Even though the CNN performs well, a few modifications to the network layout can further improve the classification accuracy of the learning model. To analyse the sensor signals to generate predictions, a new deep hybrid Correlational Neural Network (CORNN) is designed and implemented in this research. The proposed detection approach and deep learning algorithm offer improved accuracy when compared to other non-invasive techniques.

This paper proposes an efficient and novel approach for non-invasive diagnosis of Chronic Kidney Disease (CKD). A novel sensing module for CKD diagnosis is designed and developed in the proposed work. The concentration of urea in the saliva sample is measured for detecting the disease. A Convolutional Neural Network-Support Vector Machine (CNN-SVM) hybrid deep learning model is implemented for making predictions by extracting and classifying the features from the sensor response. Experiments are conducted to test the proposed model with real-time samples and to compare its performance with conventional CNN. The experimental results indicate that the proposed model outperforms traditional data classification techniques. The proposed hybrid model achieved a prediction accuracy of 96.59 %.

In this paper, we propose a novel approach for detecting Covid-19 from exhaled breath. Recent studies have revealed that certain exhaled breath components can be used as biomarkers for detecting Covid-19. Breath-based analysis stands out among the various non-invasive ways of detection as it provides a simpler and more relaxed method of diagnosis. Low-cost diagnosis methods are required to prevent the spread of infectious diseases like Covid-19. A cost-effective sensing module consisting of an array of sensors is developed in this work to measure the levels of acetone, methanol, isopropanol and ethanol in the exhaled breath to detect Covid-19. For the best predictive performance, we have implemented a Convolution Neural Network-Categorical Boosting (CNN-CatBoost) ensemble model, which classifies the test samples to make predictions. The proposed procedure correctly classified the samples with an accuracy of 96.15%. The results of the study suggest that the combinations of these four biomarkers, along with the proposed sensing module and deep learning technique, can serve as a trustworthy method for non-invasively detecting Covid-19.