INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.5, No.11, Oct. 2013

A Framework for Selection of Membership Function Using Fuzzy Rule Base System for the Diagnosis of Heart Disease

Full Text (PDF, 585KB), PP.62-70


Views:154   Downloads:3

Author(s)

Manisha Barman, J Paul Chaudhury

Index Terms

Fuzzy Logic, Membership Function, Fuzzy Rule Base System

Abstract

Today’s technology prediction of a heart disease using intelligent system is a real challenge to modern technology. In this paper different membership functions using a fuzzy rule based system for the diagnosis of the heart disease has been presented. The system has seven inputs .These are Chest pain type, resting blood pressure in mm(Trestbps),Serum cholesterol in mg(Chol),numbers of years as a smoker(years), fasting of blood sugar(fbs), maximum heart rate achieved(thalrest), resting blood rate(tpeakbps). The angiographic disease status of heart of patients has been recorded as an output. It is to mention that the diagnosis of heart disease by angiographic disease status is assigned by a number between 0 to 1,that number indicates whether the heart attack is mild or massive. Here an effort has been made to decide suitable membership function for proper diagnosis of heart disease.Different membership functions used are triangular, trapezoidal, Gaussian ,Z shaped, bell shaped ,sigmoid based ,Gaussians combination membership functions. Based on the minimum value of absolute residual the particular membership function can be decided using the fuzzy rule base system for the proper diagnosis heart disease status of a patient.

Cite This Paper

Manisha Barman, J Paul Chaudhury,"A Framework for Selection of Membership Function Using Fuzzy Rule Base System for the Diagnosis of Heart Disease", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.11, pp.62-70, 2013. DOI: 10.5815/ijitcs.2013.11.07

Reference

[1]Resul Das a, Ibrahim Turkoglu b, Abdulkadir Sengur b; “Effective diagnosis of heart disease through neural networks ensembles ", www.elsevier.com/locate/eswa , Expert systems with applucations Vol 36 number 4, May , 2009, ISSN0957–4174,page no 7675–7680

[2]Vanisree K,Jyothi Singaraju,”Decision Support System for Congenital HeartDisease Diagnosis based on Signs and Symptoms using Neural Networks”, International Journal of Computer Applications (0975 – 8887),volume 19– No.6, April 2011. page no 6-12

[3]PritiSrinivas Sajja,Dipti M shah, " Knowledgebased Diagnosis of Abdomen Pain using Fuzzy Prolog Rules”, Journal of EmergingTrends in Computing and Information science”, vol 1,no.2, Oct 2010, E-ISSN2218-6301,page no 55-60

[4]Ali.Adeli, Mehdi.Neshat ," A Fuzzy Expert System for Heart Disease Diagnosis” Proceedings of the International Multi Conference of Engineers and computer scientists 2010 vol 1, ISBN 978-988-17012-8-2,ISSN 2078-0958, March 2010,page no136-139.

[5]Narendra S. Chaudhuri and Avishek Ghosh,”Feature Extraction using fuzzy rule base system”, International Journal of Computer Science and Applications”, “Vol. 5, No. 3, page no 1 – 8”, 

[6]Ranjana Raut, S. V. Dudul,”Intelligent Diagnosis of Heart Diseases using Neural Network Approach”,International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 2,page no 97-102

[7]V. Sundarapandian, E.P.Ephzibah,” Framing Fuzzy Rules using support sets for Effective Heart Disease Diagnosis”, International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.1, February 2012,page no 11-16

[8] Novruz Allahverdi, Serhat Torun, Ismail Saritas,” Design of a Fuzzy Expert System for Determination of Coronary Heart Disease Risk”,International Conference on Computer Systems and Technologies - CompSysTech’07,page no IIIA.14-5to - IIIA.14-8 

[9]Jyoti Soni, Ujma Ansari, Dipesh Sharma,Sunita Soni,” Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”,International Journal of Computer Applications (0975 – 8887)Volume 17– No.8, March 2011,page no 43-48

[10]K. Polat & S. Sahan & S. Güne,” A new method to medical diagnosis: artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia”, Expert Systems with Applications 31 (2) (2006) ,page no264–269

[11]“archieve.ics.uci.edu/ml/datasets K.Usha Rani,” Analysis of heart Disease dataset using neural Network Technique”,Inaternational Journal of Data Mining & Knowledge Management Process(IJDKP), Vol.1,No.5, September 2011, page no 1-8.

[12]K.Rajeswari,V.vaithiyanatham,P.Amirtharaj,”Prediction of Risk score for Heart Disease in india using Machine Intelligence”,International Conference on Information and network Technology 2011,IPCSIT Press,Singapore,vol no 4,page no 18-22.

[13]Ersin Kaya, Bulent Oran and Ahmet Arslan,”A Diagnostic fuzzy rule Based System for Congential Heart Disesae”, World Academy of Science, Engineering and Technology, 54 2011, page no 252-256

[14]E.P.Ephzibah1, V. Sundarapandian,” A Neuro Fuzzy Expert System for Heart Disease Diagnosis”, Computer Science & Engineering: An International Journal (CSEIJ), Vol.2, No.1, February 2012,page no 17-23

[15]Shradhanjali Rout,” Fuzzy Petri Net Application: Heart Disease Diagnosis”, procedings of International Conference on Computing and Control Engineering (ICCCE 2012), April, 2012, ISBN 978-1-4675-2248-Published by Coimbatore Institute of Information Technology,Page no 1-9