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Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches

Sudhir Anakal1 , Chandrasekhar Uppin2 , Ambresh Bhadrashetty3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 907-910, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.907910

Online published on Jan 31, 2019

Copyright © Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty . 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: Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty, “Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.907-910, 2019.

MLA Style Citation: Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty "Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches." International Journal of Computer Sciences and Engineering 7.1 (2019): 907-910.

APA Style Citation: Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty, (2019). Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches. International Journal of Computer Sciences and Engineering, 7(1), 907-910.

BibTex Style Citation:
@article{Anakal_2019,
author = {Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty},
title = {Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {907-910},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3607},
doi = {https://doi.org/10.26438/ijcse/v7i1.907910}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.907910}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3607
TI - Smartphone based Ischemic Heart Disease (Heart Attack) Risk Prediction using Clinical Data and Data Mining Approaches
T2 - International Journal of Computer Sciences and Engineering
AU - Sudhir Anakal, Chandrasekhar Uppin, Ambresh Bhadrashetty
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 907-910
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

We designed a mobile application to deal with Ischemic Heart Disease (IHD) (Heart Attack) An Android based mobile application has been used for coordinating clinical information taken from patients suffering from Ischemic Heart Disease (IHD). The clinical information from 787 patients has been investigated and associated with the hazard factors like Hypertension, Diabetes, Dyslipidemia (Abnormal cholesterol), Smoking, Family History, Obesity, Stress and existing clinical side effect which may propose basic non-identified IHD. The information was mined with information mining innovation and a score is produced. Effects are characterized into low, medium and high for IHD. On looking at and ordering the patients whose information is acquired for producing the score; we found there is a noteworthy relationship of having a heart occasion when low and high and medium and high class are analyzed; p=0.0001 and 0.0001 individually. Our examination is to influence straightforward way to deal with recognize the IHD to risk and careful the population to get themselves assessed by a cardiologist to maintain a strategic distance from sudden passing. As of now accessible instruments has a few confinements which makes them underutilized by populace. Our exploration item may decrease this constraint and advance hazard assessment on time.

Key-Words / Index Term

Heart Disease, Risk score tree, Chi-Square, p-value, IHD, Prediction Data Mining, Android, Smartphone

References

[1] A. Islam and A. Majumder, “Coronary artery disease in Bangladesh: A review”, Indian Heart Journal, vol. 65, no. 4, pp. 424-435, 2013.
[2] M. Abu Sayeed, H. Mahtab, S. Sayeed, T. Begum,P. Khanam and A. Banu, ”Prevalence and risk factors of coronary heart disease in a rural population of Bangladesh”, Ibrahim Med. Coll. J., vol. 4, no. 2, 2010.
[3] P. Wilson, R. D’Agostino, D. Levy, A. Belanger, H. Silbershatz and W. Kannel,“Prediction of Coronary Heart Disease Using Risk Factor Categories”, Circulation, vol. 97, no. 18, pp. 1837-1847, 1998.
[4] R. D’Agostino, Sr, S. Grundy, L. Sullivan,P. Wilson and for the CHD Risk Prediction Group,“Validation of the Framingham Coronary Heart Disease Prediction Scores”, JAMA, vol. 286, no. 2, p. 180, 2001.
[5] L. Burke, J. Ma, K. Azar, G. Bennett, E. Peterson,Y. Zheng, W. Riley, J. Stephens, S. Shah, B. Suffoletto, T. Turan, B. Spring, J. Steinberger and C. Quinn, “Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention”, Circulation, vol. 132, no. 12, pp. 1157-1213, 2015.
[6] I. Witten, E. Frank and M. Hall, Data mining. Burlington, MA: Morgan Kaufmann, 2011.
[7] J. Han, M. Kamber and J. Pei, Data mining. Amsterdam: Elsevier/ Morgan Kaufmann, 2012.
[8] P. Tan, M. Steinbach and V. Kumar, Introduction to data mining. Boston: Pearson Addison Wesley, 2005.
[9] “Statistical Analysis 5: Chi - squared test for 2 - way tables”, statstutor. [Online]. Available:
[10] K. Ahmed, T. Jesmin and M. Zamilur Rahman, “Early Prevention and Detection of Skin Cancer Risk using Data Mining”, International Journal of Computer Applications, vol. 62, no. 4, pp. 1-6, 2013.