Comparative Analysis of Machine Learning Methods to Detect Chronic Kidney Disease

Chronic Kidney disease (CKD) is a lifelong health hazard that can cause the failure of kidneys. Symptoms of this develop slowly and are not obvious. Early detection of Chronic Kidney Disease can lead to significant progress in finding the cure for this disease. Through this study, we aim to employ ML techniques for the prediction and diagnosis of Chronic Kidney Disease. The findings obtained from our predictive analysis combined with the expertise of healthcare professionals can help in making an accurate prognosis. For this, we have used a dataset containing data from 400 individuals acquired from the University of California Irvine (UCI) repository. Various feature selection techniques have been used to optimize the number of features affecting Chronic Kidney Disease. Subsequently, these desirable features are chosen and used in different ML models and their accuracy, sensitivity is compared. Multiple Machine learning algorithms have been explored such as Logistic Regression, Naïve Bayes, KNN, SVM, Decision Trees, Random Forest Classifier, and Extra Trees Classifier. It was concluded that Decision Trees using information gain gave six optimal features and the Extra Trees Classifier model gives the best accuracy of 99.36 % with Extra Trees Classifier having one of the least execution times.

• Publication year

  2021

• Venue

  Journal of Physics: Conference Series

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Physics, Computer Science

• Identifiers
  DOI 10.1088/1742-6596/1911/1/012005
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

  2023cited by this paper
• COVID-19 in Patients with Kidney Disease.

  2020cited by this paper
• In Vivo Detection of Chronic Kidney Disease Using Tissue Deformation Fields From Dynamic MR Imaging

  2019cited by this paper
• Prediction of kidney disease stages using data mining algorithms

  2019cited by this paper
• Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree

  2019cited by this paper
• Prevalence of chronic kidney disease in South Asia: a systematic review

  2018cited by this paper
• Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics

  2018influential reference
• Analysis of Chronic Kidney Disease Dataset by Applying Machine Learning Methods

  2018cited by this paper
• Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes

  2016cited by this paper
• Chronic Kidney Disease analysis using data mining classification techniques

  2016cited by this paper
• Predictive analytics for chronic kidney disease using machine learning techniques

  2016cited by this paper
• Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique

  2016cited by this paper
• Support Vector Machines for Classification

  2015cited by this paper
• Prevalence of chronic kidney disease in India - Where are we heading?

  2015cited by this paper
• Ultrasonography Image Analysis for Detection and Classification of Chronic Kidney Disease

  2012cited by this paper

• Efficient quality classification of pretreated and dehydrated carrots using feature selection and intelligent models

  2025cites this paper
• Precision Medicine in Nephrology: An Exploratory Study of Machine Learning Models for Accurate Kidney Disease Prognosis

  2024cites this paper
• Utilizing Machine Learning for Early Detection of Chronic Kidney Disease

  2024cites this paper
• Chronic Kidney Disease Prediction Using Machine Learning Algorithms

  2024cites this paper
• The development of a road network flood risk detection model using optimised ensemble learning

  2023cites this paper
• Comparative Analysis of Kidney Disease Detection Using Machine Learning

  2023influential citation
• Chronic Kidney Disease Prediction Using Machine Learning

  2023cites this paper
• Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology.

  2022cites this paper