Citation: Singh, V.; Asari, V.K.;
Rajasekaran, R. A Deep Neural
Network for Early Detection and
Prediction of Chronic Kidney Disease.
Diagnostics 2022, 12, 116. https://
doi.org/10.3390/diagnostics12010116
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 28 November 2021
Accepted: 30 December 2021
Published: 5 January 2022
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Article
A Deep Neural Network for Early Detection and Prediction of
Chronic Kidney Disease
Vijendra Singh
1,
* , Vijayan K. Asari
2
and Rajkumar Rajasekaran
3
1
School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
2
Electrical and Computer Engineering, University of Dayton, Dayton, OH 45469, USA; vasari1@udayton.edu
3
School of Computing Science and Engineering, Vellore Institute of Technology, Vellore 632014, India;
rrajkumar@vit.ac.in
* Correspondence: vsingh.fet@gmail.com
Abstract:
Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD).
Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the
world to identify CKD as a condition that leads to reduced renal function over time. A person with
CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different
diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel
deep learning model for the early detection and prediction of CKD. This research objectives to create
a deep neural network and compare its performance to that of other contemporary machine learning
techniques. In tests, the average of the associated features was used to replace all missing values in
the database. After that, the neural network’s optimum parameters were fixed by establishing the
parameters and running multiple trials. The foremost important features were selected by Recursive
Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count,
Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected
features were passed to machine learning models for classification purposes. The proposed Deep
neural model outperformed the other four classifiers (Support Vector Machine (SVM), K-Nearest
Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100%
accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD.
Keywords:
chronic kidney disease; feature selection; recursive feature elimination; support vector
machine; machine learning
1. Introduction
Chronic kidney disease is a disorder that occurs when a patient’s kidney function
deteriorates. As a result, their overall quality of life suffers. Chronic kidney disease
affects one out of every 10 people worldwide (CKD). CKD is on the rise, and by 2040, it is
expected to be the fifth leading cause of death worldwide [
1
]. It is one of the leading causes
of high medical costs. In high-income nations, the cost of transplantation and dialysis
accounts for 2% to 3% of the annual medical budget [
2
]. Most people with renal failure
in low- and middle-income countries have insufficient access to life-saving dialysis and
kidney transplants [
3
]. The number of kidney failure cases is expected to rise unexpectedly
in developing countries such as China and India [
4
]. Chronic kidney failure makes to
difficulties in removing extra fluids from the body blood. Advanced chronic kidney disease
can cause dangerous levels of fluid, electrolytes, and wastes to build up in the body. It may
lead to complications such as high blood pressure, anemia, weak bones, and nerve damage.
The strongest indicator of renal function is the Glomerular Filtration Rate (GFR) [
5
]. Doctors
also determine kidney disease through glomerular filtration rate (GFR). The criteria for
defining CKD are a kidney damage for
≥
3 months with or without decreased GFR or
glomerular filtration rate (GFR) less than 60 mL/min/1.73 m
2
for
≥
3 months with or
Diagnostics 2022, 12, 116. https://doi.org/10.3390/diagnostics12010116 https://www.mdpi.com/journal/diagnostics
