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Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning

Zeinab Gazala Rafee1 , Gowramma G S2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1010-1013, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.10101013

Online published on Jun 30, 2018

Copyright © Zeinab Gazala Rafee, Gowramma G S . 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: Zeinab Gazala Rafee, Gowramma G S, “Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1010-1013, 2018.

MLA Style Citation: Zeinab Gazala Rafee, Gowramma G S "Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning." International Journal of Computer Sciences and Engineering 6.6 (2018): 1010-1013.

APA Style Citation: Zeinab Gazala Rafee, Gowramma G S, (2018). Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning. International Journal of Computer Sciences and Engineering, 6(6), 1010-1013.

BibTex Style Citation:
@article{Rafee_2018,
author = {Zeinab Gazala Rafee, Gowramma G S},
title = {Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {1010-1013},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2290},
doi = {https://doi.org/10.26438/ijcse/v6i6.10101013}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.10101013}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2290
TI - Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning
T2 - International Journal of Computer Sciences and Engineering
AU - Zeinab Gazala Rafee, Gowramma G S
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 1010-1013
IS - 6
VL - 6
SN - 2347-2693
ER -

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Abstract

In this research paper we are going to show how we can predict heart attack diseases and condition by applying patient data to developed software. Early techniques have not been so much efficient in finding it even medical professors are not so much efficient enough in predicting the heart disease. We have plan to solve related this concept by this paper, here some of pre-defined heart related are stored in databases, according those databases value our algorithms (K-means) detect patient condition related heart issues, including Heart Attack, Stroke, Congestive Heart Failure, Angina and Cardiovascular Disease this is very help tool which is used by all humans in any diagnostics or hospitals, here we have plan to upgrade emergency doctor contact sharing technique, if any patient want immediate response from doctors they can take some immediate suggestion form doctors by data sharing technique. Finally, we have concluded to introduce some challenging issues in the design of efficient auditing protocols for prediction heart condition.

Key-Words / Index Term

Heart Attack, Predefined Parameters, Machine Learning, Patient Records, Doctors Records, Heart Disease, Prediction Model

References

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