Systematic Review on Chronic Kidney Disease Detection Using Deep Learning

Chronic Kidney Disease remains a public health problem despite advances in surgical treatments and treatments, and the recent rise in CKD has led to increased diagnosis among researchers worldwide. There is great interest in developing powerful methods for healing and preventive therapy. It can improve performance by learning features that deal with problems. In addition to laboratory tests, analyzing a patient's medical data can help medical partners predict early stages of disease. Numerous attempts have been made to develop intelligent systems that analyze health data and predict CKD, but the performance of these systems still needs improvement. In this analysis, a hybrid technique was used to create the models. The model that Pearson correlation uses to choose features. Critical literature review served as the foundation for the initial selection of the best model. This analysis's goal is to assess the top Deep Learning classification techniques and produce better outcomes. This achieves the highest accuracy that provides better results.