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Heart Disease Risk Predictor System using Data Mining Learning Techniques: Analysis

by Ashutosh Kumar Singh, Asmita Dixit, Aatif Jamshed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 23
Year of Publication: 2020
Authors: Ashutosh Kumar Singh, Asmita Dixit, Aatif Jamshed
10.5120/ijca2020920198

Ashutosh Kumar Singh, Asmita Dixit, Aatif Jamshed . Heart Disease Risk Predictor System using Data Mining Learning Techniques: Analysis. International Journal of Computer Applications. 176, 23 ( May 2020), 23-30. DOI=10.5120/ijca2020920198

@article{ 10.5120/ijca2020920198,
author = { Ashutosh Kumar Singh, Asmita Dixit, Aatif Jamshed },
title = { Heart Disease Risk Predictor System using Data Mining Learning Techniques: Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 23 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number23/31339-2020920198/ },
doi = { 10.5120/ijca2020920198 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:18.417448+05:30
%A Ashutosh Kumar Singh
%A Asmita Dixit
%A Aatif Jamshed
%T Heart Disease Risk Predictor System using Data Mining Learning Techniques: Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 23
%P 23-30
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper author throws the light on how the cast amount of data developed by healthcare firms is not utilized properly for making better decisions. The main objective of this research is developing such a model Intelligent Heart Disease Prediction System (IHDPS) with the help of three data mining modelling techniques, namely, Decision Trees prediction model, Naïve Bayes probability technique and Neural Network. The Intelligent Heart Disease Prediction System (IHDPS) enables better approach in finding and extricating hidden knowledge (patterns and relationships) associated with heart disease from previous heart disease repository. It enables answering complex queries for diagnosing heart disease and thus assisting in healthcare practitioners. It helps in making intelligent clinical decisions.

References
  1. Palaniappan, S. and Awang, R., 2008, March. Intelligent heart disease prediction system using data mining techniques. In 2008 IEEE/ACS international conference on computer systems and applications (pp. 108-115). IEEE.
  2. Sultana, M., Haider, A. and Uddin, M.S., 2016, September. Analysis of data mining techniques for heart disease prediction. In 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-5). IEEE.
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  6. Pattekari, S.A. and Parveen, A., 2012. Prediction system for heart disease using Naïve Bayes. International Journal of Advanced Computer and Mathematical Sciences, 3(3), pp.290-294.
  7. Subbalakshmi, G., Ramesh, K. and Rao, M.C., 2011. Decision support in heart disease. Indian Journal of Computer Science and Engineering (IJCSE), 2(2), pp.170-176.
  8. Taneja, A., 2013. Heart disease prediction system using data Oriental Journal of Computer science and technology, 6(4), pp.457-466.
  9. Chitra, R. and Seenivasagam, V., 2013. Review of heart disease prediction system using data mining and hybrid intelligent techniques. ICTACT journal on soft computing, 3(04), pp.605-09
Index Terms

Computer Science
Information Sciences

Keywords

Heart Disease MVT Django Logic Regression

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