International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 4, No. 9, 8 Aug. 2012
Cover page and Table of Contents: PDF (size: 478KB)
Development of Neuro-fuzzy System for Early Prediction of Heart Attack
Full Text (PDF, 478KB), PP.22-28
Views: 0 Downloads: 0
Author(s)
Index Terms
ANFIS, Adaptative Neuro-Fuzzy System, Fuzzification, Membership Function, Fuzzy Rule
Abstract
This work is aimed at providing a neuro-fuzzy system for heart attack detection. Theneuro-fuzzy system was designed with eight input field and one output field. The input variables are heart rate, exercise, blood pressure, age, cholesterol, chest pain type, blood sugar and sex. The output detects the risk levels of patients which are classified into 4 different fields: very low, low, high and very high. The data set used was extracted from the database and modeled in order to make it appropriate for the training, then the initial FIS structure was generated, the network was trained with the set of training data after which it was tested and validated with the set of testing data. The output of the system was designed in a way that the patient can use it personally. The patient just need to supply some values which serve as input to the system and based on the values supplied the system will be able to predict the risk level of the patient.
Cite This Paper
Obanijesu Opeyemi, Emuoyibofarhe O. Justice, "Development of Neuro-fuzzy System for Early Prediction of Heart Attack", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.9, pp.22-28, 2012. DOI:10.5815/ijitcs.2012.09.03
Reference
[1]American Heart Association, Inc. (2011), “Sudden Cardiac Arrest” National Heart Lung and Blood Institute U.S. Department of Health and Human Services.
[2]Ali A. and Mehdi N. (2010),“A Fuzzy Expert System for Heart Disease Diagnosis” A Proceeding of the International MultiConference of Engineers and Computer Scientists, vol. 1
[3]ZAPTRON Systems, Inc. Neurofuzzy(1999),”A Different Type of Neural Nets” Zaptron's High-Order Nonlinear Neural Networks.|Fuzzy logic|Neural.
[4]Mehdi N. and Mehdi Y. (2009),” Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System” A Proceeding of the World Congress on Engineering and Computer Science, vol. II.
[5]Vipul A.S.,(2009),”Adaptive Neuro-Fuzzy Inference System for Effect of Wall Capacitance in a Batch Reactor” Advances in Fuzzy Mathematics ISSN 0973-533X 4;69-75, 69-70
[6]Jyh-Shing and Roger Jang.(1993),” ANFIS: Adaptive-Network-Based Fuzzy Inference System, computer methods and programs in biomedicine”, IEEE Transactions on Systems, University of California.
[7]Nazmy T.M., El-messiry H., Al-bokhity B. (2009),” Adaptive neuro-fuzzy inference system for, classification of ECG signals “ Journal of Theoretical and Applied Information Technology pp 71-73
[8]Robert D., Matthias P., Williams S., Andras J. ,UCI repository of Machine Learning Databases,University of Califonia. Available online at: www.archive.ics.uci.edu/ml/datasets/ Heart+disease.