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Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques

Published on February 2016 by Poonam Rahul Hankare, Hemalata A. Gosavi
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International Conference on Advances in Science and Technology
Foundation of Computer Science USA
ICAST2015 - Number 1
February 2016
Authors: Poonam Rahul Hankare, Hemalata A. Gosavi
4d3b41c5-2727-4c10-8a34-a23a672af2e8

Poonam Rahul Hankare, Hemalata A. Gosavi . Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques. International Conference on Advances in Science and Technology. ICAST2015, 1 (February 2016), 16-19.

@article{
author = { Poonam Rahul Hankare, Hemalata A. Gosavi },
title = { Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques },
journal = { International Conference on Advances in Science and Technology },
issue_date = { February 2016 },
volume = { ICAST2015 },
number = { 1 },
month = { February },
year = { 2016 },
issn = 0975-8887,
pages = { 16-19 },
numpages = 4,
url = { /proceedings/icast2015/number1/24219-3004/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Science and Technology
%A Poonam Rahul Hankare
%A Hemalata A. Gosavi
%T Medicinal Decision Support System for Cardiovascular Disease using Data Mining Techniques
%J International Conference on Advances in Science and Technology
%@ 0975-8887
%V ICAST2015
%N 1
%P 16-19
%D 2016
%I International Journal of Computer Applications
Abstract

Restorative science industry has tremendous measure of information, however shockingly the vast majority of this information is not mined to discover out shrouded data in information. Propelled information mining systems can be utilized to find shrouded design in information. Models created from these systems will be valuable for medicinal professionals to take successful choice. In this examination paper, one of the information mining arrangement system Decision Tree calculation C4. 5, ID3 and CART are dissected on cardiovascular illness dataset. Exhibitions of these calculations are thought about through affectability, specificity, exactness, blunder rate, True Positive Rate and False Positive Rate. In our studies 10-fold cross acceptance system was utilized to gauge the impartial evaluation of these expectation models. According to our outcomes, mistake rates for Decision Tree calculation C4. 5, ID3 and CART are 02. 756, 0. 2755 and 0. 2248 individually. Exactness of Decision Tree calculation C4. 5, ID3 and CART are 80. 06%, 81. 08% and 84. 12% individually. Our examination demonstrates that out of these three order method Decision Tree calculation CART predicts cardiovascular illness with minimum mistake rate and most astounding precision.

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Index Terms

Computer Science
Information Sciences

Keywords

Active Learning Decision Support System Data Mining Medical Engineering C4. 5 Id3 And Cart.