Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine

Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine

Chronic Kidney Disease (CKD) is when the kidneys are no longer working normally as they used to be, and filtering the blood was one of their obligations. The condition is classified as "chronic"since the kidney damage occurs gradually over time. This will cause waste to build up in the bod...

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Published in:2022 IEEE 10th Conference on Systems, Process and Control, ICSPC 2022 - Proceedings
Main Author: Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146662869&doi=10.1109%2fICSPC55597.2022.10001806&partnerID=40&md5=48ea4aae8b5201c04b23ff2db6c35187
id 2-s2.0-85146662869
spelling 2-s2.0-85146662869
Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
2022
2022 IEEE 10th Conference on Systems, Process and Control, ICSPC 2022 - Proceedings


10.1109/ICSPC55597.2022.10001806
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146662869&doi=10.1109%2fICSPC55597.2022.10001806&partnerID=40&md5=48ea4aae8b5201c04b23ff2db6c35187
Chronic Kidney Disease (CKD) is when the kidneys are no longer working normally as they used to be, and filtering the blood was one of their obligations. The condition is classified as "chronic"since the kidney damage occurs gradually over time. This will cause waste to build up in the body. This study is aimed to predict the stages suffered by Chronic Kidney Disease patients, which might help in early detection and prevention. A Support Vector Machine (SVM) serves as the foundation for the prediction system developed by MathWorks and the missing data analysis will be done by using IBM SPSS Statistic 21. The work will show the feature selection and classification-based methods to enhance the performance accuracy of the algorithm in giving effective analysis and prediction of Chronic Kidney Disease. In conclusion, the accuracy achieved by using SVM with 50% holdout validation had the highest accuracy percentage of 93.5% out of other types of validation involved for the analysis of the datasets. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
spellingShingle Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
author_facet Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
author_sort Bin Abdul Ghafar M.H.; Aleena Binti Abdullah N.; Abdul Razak A.H.; Syahirul Amin Bin Megat Ali M.; Mutalib Al-Junid S.A.
title Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
title_short Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
title_full Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
title_fullStr Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
title_full_unstemmed Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
title_sort Chronic Kidney Disease Prediction based on Data Mining Method and Support Vector Machine
publishDate 2022
container_title 2022 IEEE 10th Conference on Systems, Process and Control, ICSPC 2022 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/ICSPC55597.2022.10001806
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146662869&doi=10.1109%2fICSPC55597.2022.10001806&partnerID=40&md5=48ea4aae8b5201c04b23ff2db6c35187
description Chronic Kidney Disease (CKD) is when the kidneys are no longer working normally as they used to be, and filtering the blood was one of their obligations. The condition is classified as "chronic"since the kidney damage occurs gradually over time. This will cause waste to build up in the body. This study is aimed to predict the stages suffered by Chronic Kidney Disease patients, which might help in early detection and prevention. A Support Vector Machine (SVM) serves as the foundation for the prediction system developed by MathWorks and the missing data analysis will be done by using IBM SPSS Statistic 21. The work will show the feature selection and classification-based methods to enhance the performance accuracy of the algorithm in giving effective analysis and prediction of Chronic Kidney Disease. In conclusion, the accuracy achieved by using SVM with 50% holdout validation had the highest accuracy percentage of 93.5% out of other types of validation involved for the analysis of the datasets. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
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record_format scopus
collection Scopus
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