International Journal of Current Research and Review (IJCRR)

Source ID:00000201
Volume 13, Issue 6,March 2021, Pages 161-166, Page Count - 6

Classification of Algorithms Supported Factual Knowledge Recovery from Cardiac Data Set

M. Sivakami (1) P. Prabhu (2)

(1) Research Scholar, Department of Computer Applications, Alagappa University, Karaikudi, India.
(2) Research Supervisor, Assistant Professor of IT (DDE), Alagappa University, Karaikudi, India.

Manuscript ID:- 00000-74345
Access Type : Open Access
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Cite this article:M. Sivakami,P. Prabhu,  Classification Of Algorithms Supported Factual Knowledge Recovery From Cardiac Data Set, International Journal of Current Research and Review (IJCRR), 2021, 13(6), PP.161-166

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Introduction: Improvised modern lifestyle with more fascination towards fast food causes severe anxieties over human health standards. This renders the society to visit the physicians often, which in turn generates terabytes of diagnostic data. The stored data on critical mining using algorithm provides a wealth of information to clinicians and back them to execute a better treatment. Heart disease rank’s first among the charted ailments due to its life-threatening concerns.

Objectives: In the present work mining of cardiac data sets obtained from the University of California Irvine (UCI) repository was done using algorithms such as Linear Regression, Naive Bayes and Decision Stump algorithms in Waikato Environment for Knowledge Analysis (WEKA) environment.

Result and Conclusion: The obtained results concluded that the Naive Bayes classifier offered the highest accuracy with specificity among the studied algorithms.
Author Keywords
Decision stump Heart disease Linear regression Mining Naive Bayes
This research work was carried out with the financial support of the RUSA-Phase 2.0 grant sanctioned vide Letter No. F24-51 / 2014-U, Policy (TNMulti-Gen) Dept. of Edn. Govt of India, Dt.09.10.2018 at Alagappa University, Karaikudi, Tamilnadu, India

ISSN Print: 2231-2196 ISSN Online: 0975-5241
Source Type: Journals Document Type: Journal Article
Publication Language: English DOI:
Abbreviated Journal Title: IJCRR Access Type: Open Access
Publisher Name: Radiance Research Academy Resource Licence: CC BY-NC
Major Subject:Physical Sciences Subject Area classification: Computer Science
Subject area: Computer Science Applications Source: SCOPEDATABASE