A Deep Learning Approach for Predicting Chronic Kidney Disease in Electronic Health Records in Neural Nephrology

Abstract

The prevalence of Chronic Kidney Disease (CKD) presents a formidable global health challenge, necessitating swift and accurate diagnosis for effective intervention. This research employs image processing techniques to automate CKD detection and classification, focusing on four distinct classes: normal, tumour, cyst, and stone. Leveraging a labelled image dataset, this study endeavours to mitigate the limitations of conventional diagnostic approaches through computational analysis of renal images. By preprocessing the dataset to enhance image quality and extracting pertinent features, we aim to facilitate the classification process using machine learning algorithms. Through advanced image processing methods, this research aims to achieve heightened diagnostic accuracy and efficiency in distinguishing between different CKD classes. Sample metrics, including sensitivity, specificity, accuracy, and area under the curve (AUC), will be utilised to evaluate the performance of the automated classification system. This endeavour holds promise in augmenting current diagnostic practices, contributing to improved patient outcomes and streamlined healthcare delivery in Chronic Kidney Disease.