NDLI: Risk assessment of coronary arteries heart disease based on neuro-fuzzy classifiers

Content Provider	IEEE Xplore Digital Library
Author	Zand, M.D. Ansari, A.H. Lucas, C. Zoroofi, R.A.Z.
Copyright Year	2010
Description	Author affiliation: Department of Electrical & Computer Engineering, University of Tehran, Iran (Zand, M.D.; Ansari, A.H.; Zoroofi, R.A.Z.) \|\| Control & Intelligent Processing Center of Excellence, University of Tehran, Iran (Lucas, C.)
Abstract	Coronary artery heart disease is one of the main reasons of death in under development countries such as Iran. Based on vagueness in data and uncertainty in decision making finding an optimal way for diagnosis would be helpful. The main goal of this article is to change the linguistic terms which doctors use for representing coronary heart disease possibility into classified stages. Indeed the medical diagnosis can be used as an issue of pattern recognition by an input vector of features introduced to a system so the corresponding risk of a particular disease can be estimated with this system. In this paper we used an adaptive network-based inference system in order to assess the risk of coronary artery heart diseases .With this point of view that fault in medical diagnosis are more errors of omission than of commission, we are going to reduce this diagnosis defect with Neuro-fuzzy networks such as Multi-Layer Perceptron (MLP) and an inference Neuro-fuzzy network such as ANFIS. In the last section we will compare these methods to show that ANFIS can obtain the best accommodation with doctor's opinions.
Starting Page	1
Ending Page	4
File Size	853249
Page Count	4
File Format	PDF
ISBN	9781424474837
e-ISBN	9781424474844
DOI	10.1109/ICBME.2010.5705028
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-03
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Clasification Coronary Artery heart disease Risk Hardware design languages Neuro-Fuzzy Network Diseases
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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