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Prediction of Women’s Diabetic Disorder Using R Tool

G. Kanimozhi1 , S. Nalini2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 1008-1011, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.10081011

Online published on Mar 31, 2019

Copyright © G. Kanimozhi, S. Nalini . 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: G. Kanimozhi, S. Nalini, “Prediction of Women’s Diabetic Disorder Using R Tool,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1008-1011, 2019.

MLA Style Citation: G. Kanimozhi, S. Nalini "Prediction of Women’s Diabetic Disorder Using R Tool." International Journal of Computer Sciences and Engineering 7.3 (2019): 1008-1011.

APA Style Citation: G. Kanimozhi, S. Nalini, (2019). Prediction of Women’s Diabetic Disorder Using R Tool. International Journal of Computer Sciences and Engineering, 7(3), 1008-1011.

BibTex Style Citation:
@article{Kanimozhi_2019,
author = {G. Kanimozhi, S. Nalini},
title = {Prediction of Women’s Diabetic Disorder Using R Tool},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {1008-1011},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3956},
doi = {https://doi.org/10.26438/ijcse/v7i3.10081011}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.10081011}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3956
TI - Prediction of Women’s Diabetic Disorder Using R Tool
T2 - International Journal of Computer Sciences and Engineering
AU - G. Kanimozhi, S. Nalini
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 1008-1011
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

This Project is Modern Medicine generates a great deal of information which is deserted into the medical dataset. The proper analysis of such information may reveal some interesting facts, which may otherwise be hidden or go dissipate data analytics is one such field which tries to extract some interesting facts from a huge dataset. In this project, an attempt is made to analyze the diabetic dataset and drive some interesting facts from it which can be a prediction model. Random forest algorithm builds in multiple decision trees and merges them to get a more accurate and stable prediction. A huge medical dataset accessible in different data repositories used in the real world application. This aim of this project is to give a detailed version predictive models from base to state-of-art, describing predictive models, steps to develop a predictive model for determining diabetic disorder.

Key-Words / Index Term

Diabetic disorder, Classification, Prediction, And Random Forest

References

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[2] Sadhana, Savitha Shetty, “Analysis of Diabetic Data Set Using Hive and R ", International Journal of Emerging Technology and Advanced Engineering, 2014.

[3] Dr. Saravana Kumar, Eswari, Sampath & Lavanya," Predictive Methodology for Diabetic Data Analysis in Big Data", ELSEVIER, 2015.

[4] Rahul Joshi, Minyechil Alehegn, "Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach" International Research Journal of Engineering and Technology (IRJET) , 2017.

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[7] Allen Daniel Sunny1, Sajal Kulshreshtha2, Satyam Singh3, Srinabh4, Mr. Mohan Ba5, Dr. Sarojadevi H.6,” Disease Diagnosis System By Exploring Machine Learning Algorithms”, International Journal of Innovations in Engineering and Technology (IJIET), Volume 10 Issue 2 May 2018
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