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

doi:10.1109/IC2E357697.2023.10262425

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 proceeding › Conference contribution › peer-review

6 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 language	English
Title of host publication	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350338003
DOIs	https://doi.org/10.1109/IC2E357697.2023.10262425
Publication status	Published - 2023
Externally published	Yes
Event	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 - Srinagar Garhwal, India Duration: 8 Jun 2023 → 9 Jun 2023

Publication series

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

Conference

Conference	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Country/Territory	India
City	Srinagar Garhwal
Period	8/06/23 → 9/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

Access to Document

10.1109/IC2E357697.2023.10262425

Cite this

Vijayvargiya, A., Raghav, A., Bhardwaj, A., Gehlot, N., & Kumar, R. (2023). A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease. In 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC2E357697.2023.10262425

Vijayvargiya, Ankit ; Raghav, Aarsh ; Bhardwaj, Anchal et al. / A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease. 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023).

@inproceedings{49ea1a446e0c41f8a75604556d958ac1,

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

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.",

keywords = "Chronic Kidney Disease (CKD), Explainable Artificial Intelligence (XAI), Machine Learning (ML), Random Forest (RF)",

author = "Ankit Vijayvargiya and Aarsh Raghav and Anchal Bhardwaj and Naveen Gehlot and Rajesh Kumar",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 ; Conference date: 08-06-2023 Through 09-06-2023",

year = "2023",

doi = "10.1109/IC2E357697.2023.10262425",

language = "English",

series = "2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023",

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Vijayvargiya, A, Raghav, A, Bhardwaj, A, Gehlot, N & Kumar, R 2023, A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease. in 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023, Srinagar Garhwal, India, 8/06/23. https://doi.org/10.1109/IC2E357697.2023.10262425

A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease. / Vijayvargiya, Ankit; Raghav, Aarsh; Bhardwaj, Anchal et al.
2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease

AU - Vijayvargiya, Ankit

AU - Raghav, Aarsh

AU - Bhardwaj, Anchal

AU - Gehlot, Naveen

AU - Kumar, Rajesh

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Chronic Kidney Disease (CKD)

KW - Explainable Artificial Intelligence (XAI)

KW - Machine Learning (ML)

KW - Random Forest (RF)

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M3 - Conference contribution

AN - SCOPUS:85174549797

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

Y2 - 8 June 2023 through 9 June 2023

ER -

Vijayvargiya A, Raghav A, Bhardwaj A, Gehlot N, Kumar R. A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease. In 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023). doi: 10.1109/IC2E357697.2023.10262425

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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