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Identifying Oversampling and under sampling of Data-A Practical Approach Using R

V. Shobana1 , K. Nandhini2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 890-896, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.890896

Online published on May 31, 2019

Copyright © V. Shobana, K. Nandhini . 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: V. Shobana, K. Nandhini, “Identifying Oversampling and under sampling of Data-A Practical Approach Using R,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.890-896, 2019.

MLA Style Citation: V. Shobana, K. Nandhini "Identifying Oversampling and under sampling of Data-A Practical Approach Using R." International Journal of Computer Sciences and Engineering 7.5 (2019): 890-896.

APA Style Citation: V. Shobana, K. Nandhini, (2019). Identifying Oversampling and under sampling of Data-A Practical Approach Using R. International Journal of Computer Sciences and Engineering, 7(5), 890-896.

BibTex Style Citation:
@article{Shobana_2019,
author = {V. Shobana, K. Nandhini},
title = {Identifying Oversampling and under sampling of Data-A Practical Approach Using R},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {890-896},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4333},
doi = {https://doi.org/10.26438/ijcse/v7i5.890896}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.890896}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4333
TI - Identifying Oversampling and under sampling of Data-A Practical Approach Using R
T2 - International Journal of Computer Sciences and Engineering
AU - V. Shobana, K. Nandhini
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 890-896
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

The stimulation of thyroid hormones has a greater impact in maintaining the metabolism our body. If there is any misbehavior in the hormones it will affect the functioning of other organs too. It is such an important gland and proper clinical advices should be taken if there is a misbehavior. The machine learning algorithms plays a major role in the early detection of thyroid disorder. This work focuses on applying random forest algorithm in prediction of thyroid disorder. The random forest algorithm classifies the class attribute and predicts the occurrence of hypo or hyper or normal scenario of thyroid. The algorithm predicts the result with maximum accuracy. The work is implemented in R. R is a statistical tool and it very much handles large volumes of data compared to other traditional mining tools. The algorithm predicts more accurately and the various performance metrics has been analysed.The data set has been taken from UCI Machine repository.

Key-Words / Index Term

Thyroid, random forest, big data, R studio, Confusion Matrix

References

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