An Ensemble Learning Approach for detection of Chronic Kidney Disease (CKD)

 

B. Narasimha Swamy1, Rajeswari Nakka2, Aditi Sharma3*,6, S. Phani Praveen4, Venkata Nagaraju Thatha5, Kumar Gautam7

1,4Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, India

2Department of CSE, Seshadri Rao Gudlavalleru Engineering College, India

3Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, India

5Department of Information Technology, MLR Institute of Technology, Hyderabad

6IEEE Senior Member, Symbiosis International University, Pune, India

7Gwangju Institute of Science and Technology, South Korea

Emails: swamy_bn@pvpsiddhartha.ac.in; rajeswari.gec@gmail.com; aditi.sharma@ieee.org; phani.0713@gmail.com, nagaraju.thatha@gmail.com; edumonk@ieee.org

Text Box: Abstract Chronic kidney disease (CKD) is a common and possibly fatal condition affecting billions worldwide. Early detection and accurate diagnosis of CKD are critical for timely intervention and improved patient outcomes. In recent years, machine learning techniques have shown great promise in assisting medical professionals in detecting and diagnosing various diseases. This study aims to develop a novel machine learning (ML) model for detecting CKD using clinical and demographic data. The dataset used in this study comprises a comprehensive collection of patient records, including laboratory test results, medical history, and demographic information. Feature selection is one of the techniques that, combined with the ML approach, select the significant features. Several ML algorithms were implemented to detect CKD in the early stages but identified the issues with existing ML algorithms. The developed models' performance is assessed using precision, accuracy, and recall metrics. Additionally, feature importance analysis is conducted to identify the key factors influencing CKD diagnosis. The strength of the proposed approach shows accurately by identifying the individuals at risk of CKD and distinguishing between different stages of the disease. The dataset used for this research was collected from the UCI repository, which consists of 25 attributes, 550 samples, 400 CKD affected, and 150 standard models. The dataset consists of two folders, training and testing. The training utilizes 1000 samples with detailed patient health conditions. The developed CKD detection model shows promising results, achieving high accuracy of 97.98%. on the test dataset. By leveraging machine learning algorithms, this approach can assist healthcare professionals in making more informed decisions regarding early intervention and personalized treatment plans for patients with CKD. Ultimately, applying machine learning techniques in CKD detection can improve patient outcomes and reduce healthcare costs. *Corresponding Author: aditi.sharma@ieee.org


Received: April 11, 2023  Revised: July 04, 2023  Accepted: October 08, 2023

 

Keywords: Chronic kidney disease (CKD); Machine Learning (ML); Support Vector Machines (SVM); Random Forests; And Neural Networks.