Forecast of coronary heart disease using data mining classification technique

S. Chandana Yogaamrutha, D. Cenitta, R. Vijaya Arjunan

Research output: Contribution to journalArticle

Abstract

A Common heart disease is nothing but a cardiovascular disease or coronary heart disease. Predicting the presence of heart disease in prior can be of more use in saving patient’s life. Data mining, a modern technique has provided an automatic way of analyzing data using standard classification method. Classification techniques are the task of generalizing known structure to apply to new large set of data. The work incorporates the classes of heart diseases like myocardial infarction, stroke, congenital heart disease utilizing classification algorithms like support vector machine (SVM) and ensemble methods in particular stacking approach so as to see which combination of algorithms would yield high accuracy compared to other classification algorithms. This paper introduces a prediction model to forecast the presence of heart diseases considering large dataset with missing data for few attributes and makes use of data mining software called Waikato Environment for Knowledge Analysis (WEKA) which includes all techniques of classification.

Original languageEnglish
Pages (from-to)25-36
Number of pages12
JournalJournal of Advanced Research in Dynamical and Control Systems
Volume11
Issue number4
Publication statusPublished - 01-01-2019
Externally publishedYes

Fingerprint

Data mining
Support vector machines

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

Chandana Yogaamrutha, S. ; Cenitta, D. ; Vijaya Arjunan, R. / Forecast of coronary heart disease using data mining classification technique. In: Journal of Advanced Research in Dynamical and Control Systems. 2019 ; Vol. 11, No. 4. pp. 25-36.
@article{eb1c32fb59a94af5b39918e9087c057b,
title = "Forecast of coronary heart disease using data mining classification technique",
abstract = "A Common heart disease is nothing but a cardiovascular disease or coronary heart disease. Predicting the presence of heart disease in prior can be of more use in saving patient’s life. Data mining, a modern technique has provided an automatic way of analyzing data using standard classification method. Classification techniques are the task of generalizing known structure to apply to new large set of data. The work incorporates the classes of heart diseases like myocardial infarction, stroke, congenital heart disease utilizing classification algorithms like support vector machine (SVM) and ensemble methods in particular stacking approach so as to see which combination of algorithms would yield high accuracy compared to other classification algorithms. This paper introduces a prediction model to forecast the presence of heart diseases considering large dataset with missing data for few attributes and makes use of data mining software called Waikato Environment for Knowledge Analysis (WEKA) which includes all techniques of classification.",
author = "{Chandana Yogaamrutha}, S. and D. Cenitta and {Vijaya Arjunan}, R.",
year = "2019",
month = "1",
day = "1",
language = "English",
volume = "11",
pages = "25--36",
journal = "Journal of Advanced Research in Dynamical and Control Systems",
issn = "1943-023X",
publisher = "Institute of Advanced Scientific Research",
number = "4",

}

Chandana Yogaamrutha, S, Cenitta, D & Vijaya Arjunan, R 2019, 'Forecast of coronary heart disease using data mining classification technique', Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 4, pp. 25-36.

Forecast of coronary heart disease using data mining classification technique. / Chandana Yogaamrutha, S.; Cenitta, D.; Vijaya Arjunan, R.

In: Journal of Advanced Research in Dynamical and Control Systems, Vol. 11, No. 4, 01.01.2019, p. 25-36.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Forecast of coronary heart disease using data mining classification technique

AU - Chandana Yogaamrutha, S.

AU - Cenitta, D.

AU - Vijaya Arjunan, R.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - A Common heart disease is nothing but a cardiovascular disease or coronary heart disease. Predicting the presence of heart disease in prior can be of more use in saving patient’s life. Data mining, a modern technique has provided an automatic way of analyzing data using standard classification method. Classification techniques are the task of generalizing known structure to apply to new large set of data. The work incorporates the classes of heart diseases like myocardial infarction, stroke, congenital heart disease utilizing classification algorithms like support vector machine (SVM) and ensemble methods in particular stacking approach so as to see which combination of algorithms would yield high accuracy compared to other classification algorithms. This paper introduces a prediction model to forecast the presence of heart diseases considering large dataset with missing data for few attributes and makes use of data mining software called Waikato Environment for Knowledge Analysis (WEKA) which includes all techniques of classification.

AB - A Common heart disease is nothing but a cardiovascular disease or coronary heart disease. Predicting the presence of heart disease in prior can be of more use in saving patient’s life. Data mining, a modern technique has provided an automatic way of analyzing data using standard classification method. Classification techniques are the task of generalizing known structure to apply to new large set of data. The work incorporates the classes of heart diseases like myocardial infarction, stroke, congenital heart disease utilizing classification algorithms like support vector machine (SVM) and ensemble methods in particular stacking approach so as to see which combination of algorithms would yield high accuracy compared to other classification algorithms. This paper introduces a prediction model to forecast the presence of heart diseases considering large dataset with missing data for few attributes and makes use of data mining software called Waikato Environment for Knowledge Analysis (WEKA) which includes all techniques of classification.

UR - http://www.scopus.com/inward/record.url?scp=85067339186&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067339186&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85067339186

VL - 11

SP - 25

EP - 36

JO - Journal of Advanced Research in Dynamical and Control Systems

JF - Journal of Advanced Research in Dynamical and Control Systems

SN - 1943-023X

IS - 4

ER -