A Hybrid Deep Learning Approach for Chronic Kidney Disease Detection Using Convolutional Neural Networks and Support Vector Machines

In order to improve patient outcomes and reduce medical costs, chronic kidney disease (CKD), a global health concern, requires early detection. Clinical biomarkers and subjective, time-consuming physical inspection are often the foundation of conventional diagnostic methods. In this study, we propose a hybrid deep learning approach that uses Convolutional Neural Networks (CNNs) for feature extraction and Support Vector Machines (SVMs) for classification to increase the accurate identification of CKD. Following training and validation on a CKD dataset, the model achieved a notable accuracy of 96.18%. High-dimensional feature representations are effectively recovered by the CNN component, and the SVM classifier provides robust decision limits for distinguishing between CKD and non-CKD cases. Normalisation and augmentation were two preprocessing methods used to ensure data quality and model generalisability. Current advancements in AI-based CKD diagnosis are thoroughly reviewed in the literature, emphasising the value of hybrid architectures and multi-task learning for improved diagnostic performance. The area is advanced by this study, which highlights the efficacy of the CNN-SVM hybrid model and the potential of AI to automate and enhance CKD diagnosis. Findings indicate that this approach is more effective than traditional models and offers a reliable choice for clinical application.