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2008 International Conferences on Computational Intelligence for Modelling, Control and Automation; Intelligent Agents, Web Technologies and Internet Commerce; and Innovation in Software Engineering
Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool
Vienna, Austria
December 10-December 12
ISBN: 978-0-7695-3514-2
| ASCII Text | x | ||
| Elahe Yargholi, Saman Parvaneh, "Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool," Computational Intelligence for Modelling, Control and Automation, International Conference on, pp. 1199-1204, 2008 International Conferences on Computational Intelligence for Modelling, Control and Automation; Intelligent Agents, Web Technologies and Internet Commerce; and Innovation in Software Engineering, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/CIMCA.2008.172, author = {Elahe Yargholi and Saman Parvaneh}, title = {Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool}, journal ={Computational Intelligence for Modelling, Control and Automation, International Conference on}, volume = {0}, year = {2008}, isbn = {978-0-7695-3514-2}, pages = {1199-1204}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIMCA.2008.172}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Computational Intelligence for Modelling, Control and Automation, International Conference on TI - Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool SN - 978-0-7695-3514-2 SP1199 EP1204 A1 - Elahe Yargholi, A1 - Saman Parvaneh, PY - 2008 VL - 0 JA - Computational Intelligence for Modelling, Control and Automation, International Conference on ER - | |||
Cardiac risk factor assessment requires a classification system that is robust to the interaction and uncertainty of input factors, as well as being interpretable on the decision made. To meet the requirements, we made use of neuro-fuzzy methods, a certain novelty in cardiac risk assessment.Statistic data of 165 patients including sex, age, LDL, blood pressure, and Myocard-brain Creatinine Phosphokinase enzyme were collected. The intensity of infarction was determined according to the amount of the enzyme.A simplified cardiac risk stratification model was developed. Sex, age, LDL, and blood pressure were considered as the input and infarction intensity as the output. To draw the input-output mapping of each group, two hybrid neuro-fuzzy classifiers, IRIDIA Method for Neuro-fuzzy Identification and Data Analysis and Adaptive Network-based Fuzzy Inference System (ANFIS), were
Citation:
Elahe Yargholi, Saman Parvaneh, "Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool," cimca, pp.1199-1204, 2008 International Conferences on Computational Intelligence for Modelling, Control and Automation; Intelligent Agents, Web Technologies and Internet Commerce; and Innovation in Software Engineering, 2008
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