Objective: In this paper, we present a deep learning technique and a novel detection methodology to detect Chronic Kidney Disease (CKD) from saliva samples. Methods: A hybrid deep learning network comprising of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) classifier is introduced to overcome the challenges faced by conventional data classification networks. A novel dynamic pooling approach and a feature pruning algorithm are introduced in the network to select the most relevant features for the classification operation. We have examined the concentration of urea in the saliva sample to detect the disease. A new detection module is developed for testing the samples. Results: The CNN-SVM network achieved an average accuracy of 97.67% and a sensitivity and specificity of 97.5% and 97.83%, respectively. The conventional CNN model achieved an average accuracy of 96.51%. We have compared our proposed model with other existing algorithms, and it is observed that the performance achieved by this model is higher than other well-known data classification methods. Conclusion: Combining CNN with the SVM classifier enables the network to analyze the sensor data to make predictions more accurately. The use of dynamic pooling and feature pruning algorithm significantly improved the prediction accuracy of the network. The experimental results show that the proposed method provides acceptable classification accuracy and has the potential to be implemented in clinical practice. Significance: Our study result shows that the proposed methodology can be used for detecting CKD non-invasively. The proposed deep learning network provides accurate predictions compared to other data classification methods. © 2020 Elsevier Ltd