Adapting Convolutional Neural Networks for Chronic Kidney Disease Detection from Structured Data

A progressive deterioration in kidney function that often goes undiagnosed until it reaches severe stages is the hallmark of chronic kidney disease (CKD), a worldwide health concern. Preventing the development of end-stage renal disease requires an early and precise diagnosis. Conventional diagnosis techniques mostly depend on clinical knowledge and laboratory analysis, which may be laborious and unreliable in resource-constrained environments. In this work, we use the structured, tabular data from the UCI CKD dataset to suggest a novel use of Convolutional Neural Networks (CNNs) for CKD classification. By transforming the input into two-dimensional grids, CNNs may now be used to simulate spatial connections in structured datasets, despite their usual employment in image-based tasks. In order to capture feature interactions and enable the CNN to learn intricate patterns linked to CKD diagnosis, our method preprocessed the clinical data and organised them into a 2D matrix format. With an average accuracy of 96.1%, our CNN-based model proved that convolutional architectures are capable of efficiently classifying chronic kidney disease (CKD) from structured clinical data. These findings imply that CNNs, even in the absence of picture data, may be a potent substitute for conventional machine learning models in healthcare applications. The adaptability of deep learning methods and their potential to facilitate prompt, automated CKD diagnosis in clinical decision-making systems are shown by this work.