A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease

Kantesh Kumar Oad; Xu DeZhi; Pinial Khan Butt

A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease

Kantesh Kumar Oad * §

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Xu DeZhi

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Pinial Khan Butt

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Central South University

A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease

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Abstract

Health care domain systems globally face lots of difficulties because of the high amount of risk factors of heart diseases in peoples (WHO, 2013). To reduce risk, improved knowledge based expert systems played an important role and has a contribution towards the development of the healthcare system for cardiovascular disease. To make use of benefits of knowledge based system, it is necessary for health organizations and users; must need to know the fuzzy rule based expert system’s integrity, efficiency, and deployments, which are the open challenges of current fuzzy logic based medical systems. In our proposed system, we have designed a fuzzy rule based expert system and also by using data mining technique we have reduced the total number of attributes. Our system mainly focuses on cardiovascular disease diagnosis, and the dataset taken from UCI (Machine Learning Repository). We explored in the existing work. The majority of the researcher’s experimentation was made on 14 attributes out of 76. While, in our system we took advantage of 6 attributes for system design. In the preliminary stage UCI, data participated in suggested system that will get outcomes. The performance of the system matched with Neural Network and J48 Decision Tree Algorithm.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Kantesh Kumar Oad, Xu DeZhi, Pinial Khan Butt. 2014. "A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C3).

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GJCST Volume 14 Issue C3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Data mining fuzzy reasoning heart disease and diagnose

Classification

GJCST-C Classification I.5.1

Submission ReceivedDecember 6, 2013
Peer Review Double Blind
Handling Editor
Accepted January 5, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

May 27, 2014

Language

English

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Article Matrices

Total Score: 173

Country: China

Subject: Global Journal of Computer Science and Technology

Authors: Kantesh Kumar Oad, Xu DeZhi, Pinial Khan Butt (PhD/Dr. count: 0)

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Publish Date: 2014 01, Wed

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