A Deep Learning Approach to Predict Chronic Kidney Disease in Human

Abstract

Renal turmoil otherwise called Chronic Kidney Disease (CKD) has been a very important field of study for a long while now. Diagnosis of CKD requires a lot of tests and it's not a straightforward or easy process. Recent advancements in machine learning (ML) based disease classification have attracted researchers to investigate various health data. The aim of this article is to automate the detection process of CKD using clinical data by employing a deep learning (DL) model. Moreover, this study intends to achieve a robust and feasible model to detect the CKD with comprehensive clinical accuracy. Initially, preprocessing and feature engineering tasks have been performed on a dataset having 400 instances and 23 attributes. Finally, the dataset was fed to the deep learning model to classify the diagnosis of CKD. This research has obtained a higher accuracy (99%) than other recently utilized methods in CKD diagnosis by employing the deep learning model.