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A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components

Sanat Kumar Sahu1

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-9 , Page no. 66-69, Sep-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i9.6669

Online published on Sep 30, 2021

Copyright © Sanat Kumar Sahu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sanat Kumar Sahu, “A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.66-69, 2021.

MLA Style Citation: Sanat Kumar Sahu "A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components." International Journal of Computer Sciences and Engineering 9.9 (2021): 66-69.

APA Style Citation: Sanat Kumar Sahu, (2021). A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components. International Journal of Computer Sciences and Engineering, 9(9), 66-69.

BibTex Style Citation:
@article{Sahu_2021,
author = {Sanat Kumar Sahu},
title = {A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2021},
volume = {9},
Issue = {9},
month = {9},
year = {2021},
issn = {2347-2693},
pages = {66-69},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5398},
doi = {https://doi.org/10.26438/ijcse/v9i9.6669}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i9.6669}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5398
TI - A Novel Ensemble Model for Classification of Chronic Kidney Disease With Selected Features and Components
T2 - International Journal of Computer Sciences and Engineering
AU - Sanat Kumar Sahu
PY - 2021
DA - 2021/09/30
PB - IJCSE, Indore, INDIA
SP - 66-69
IS - 9
VL - 9
SN - 2347-2693
ER -

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Abstract

Diagnosis of health conditions is an incredibly difficult and significant issue in the field of medical science. Classification, dimension reduction technique (DRT), feature selection techniques (FST) play a very important role in the quick and accurate identification of disease. The chronic kidneys disease (CKD) dataset is connected into three classification methods like RF, J48 and C5.0. The proposed ensemble model (RF, J48 and C5.0) gives better accuracy i.e. 99.75% contrast with all classifiers with selected feature subset. All classification models give a better outcome with proposed PC-DRT and GA-FST when contrasted with without FST. The outcomes showed that utilizing GA-FST has computationally enhanced the classification accuracy.

Key-Words / Index Term

Classification, chronic kidneys disease, dimension reduction technique, ensemble model, feature selection techniques, genetic algorithm, principal component analysis

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