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Over 10% of the world&#x2019;s population now suffers from chronic kidney disease (CKD), and millions die yearly. CKD should be detected early to extend the lives of those suffering and lower the cost of therapy. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. This study introduces a novel hybrid deep learning network model (HDLNet) for CKD early detection and prediction. A deep learning-based technique called the Deep Separable Convolution Neural Network (DSCNN) has been suggested in this research for the early detection of CKD. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by the Capsule Network (CapsNet). Using the Aquila Optimization Algorithm (AO) method, the pertinent characteristics are selected to speed up the categorization process. The necessary features improve classification effectiveness while needing less computational effort. The DSCNN technique is optimized to diagnose kidney illness as CKD or non-CKD using the Sooty Tern Optimization Algorithm (STOA). The CKD dataset, found in the UCI machine learning repository, is then used to test the dataset. Accuracy, sensitivity, MCC, PPV, FPR, FNR, and specificity are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.

HDLNET: A Hybrid Deep Learning Network Model with Intelligent iOt for Detection and Classification of Chronic Kidney Disease