Prediction of Kidney Disease Utilizing a Hybrid Deep Learning Methodology

The kidney is an essential organ inside the human body, playing a crucial role in several physiological processes. One of its primary roles is the filtration of waste products and surplus fluids from the bloodstream, facilitating their subsequent elimination via urine production. In order to mitigate potential life-threatening complications, it is essential to identify and investigate chronic renal illnesses. The suggested method takes into account the limitations of early prediction and robust decision-making oscillations due to the presence of False Positive Rates in Kidney CT Images. The suggested system incorporates a hybrid method that combines machine learning and deep learning techniques. A prognostic method is developed by using the kidney dataset collected from TCIA, which involves the analysis of patients’ past health information. The suggested system incorporates a hybrid approach of random forest regression and multinomial regression algorithms to identify the Explorer data analysis technique for the chronic kidney disease dataset. The secondary approach involves using a computer tomography image dataset obtained from the TCIA public data set. This dataset is processed using a deep convolutional neural network (DCNN) architecture that has many layers of deep convolution filters. The purpose of this architecture is to accurately detect and classify kidney abnormalities within the images. The integration of main and secondary outcomes is used to determine the presence or absence of renal disease in a patient. In this study, we examine a unique methodology known as Hybrid Deep K-Net (H-DKN). The accuracy reached by HDKN is reported to be 97%. Furthermore, we compare this approach with many state-of-the-art methods that have been established on the current platform.