A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease

Ankit Vijayvargiya, Aarsh Raghav, Anchal Bhardwaj, Naveen Gehlot, Rajesh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Chronic Kidney Disease (CKD) is on the rise in the current research scenario. The main function of the kidney is to remove and purify the waste and blood in the human body. Diabetes is the most prevalent cause of kidney disease. The key to preventing or curing CKD is identifying it at an early stage. If early detection is avoided, there is a greater chance of kidney failure as well as heart disease, bone disease, or an imbalance in potassium and calcium levels. Prediction at an early stage for a long and healthy life is made feasible with the assistance of a machine intelligence classifier. In this study, the prediction of CKD and Non-CKD patients is done by applying five machine learning classifiers. The results show that the Random Forest classifier has the greatest accuracy of all classifiers. Explainable artificial intelligence (XAI) is introduced for a trustworthy explanation of the result. XAI investigates how the Random Forest model gives high accuracy with input features imported into the classifier.

Original languageEnglish
Title of host publication2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350338003
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 - Srinagar Garhwal, India
Duration: 8 Jun 20239 Jun 2023

Publication series

Name2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

Conference

Conference2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Country/TerritoryIndia
CitySrinagar Garhwal
Period8/06/239/06/23

Keywords

  • Chronic Kidney Disease (CKD)
  • Explainable Artificial Intelligence (XAI)
  • Machine Learning (ML)
  • Random Forest (RF)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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