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
N1 - Publisher Copyright:
© 2023 IEEE.
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)
UR - http://www.scopus.com/inward/record.url?scp=85174549797&partnerID=8YFLogxK
U2 - 10.1109/IC2E357697.2023.10262425
DO - 10.1109/IC2E357697.2023.10262425
M3 - Conference contribution
AN - SCOPUS:85174549797
T3 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
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 -