Advances in Computer Science : an International Journal
An Intelligent System based on Fuzzy Inference System to prophesy the brutality of Cardio Vascular Disease
Abstract
To unravel hidden relationships and diagnose diseases efficiently, Data Mining along with Soft Computing Techniques are used in several researches. Cardio Vascular Disease is a condition which leads to severe disability and death. Since the diagnosis involves vague symptoms and tedious procedures, diagnosis is usually time-consuming and erroneous. For the healthier analysis and treatment of heart disease based on brutality, an Intellectual, accurate and proficient investigative system is needed. For diagnosing heart disease with improved effectiveness, an Intelligent Fuzzy Inference System is needed. This paper illustrates how Fuzzy Inference System is used to envisage the severity of disease by constructing an effective Fuzzy Rule Base. It is also proved that a precision of 95.23% is obtained when Fuzzy System is used in severity prediction
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www.nytimes.com/2009/07/29/opinim/29hall.html.
K.Rajeswari, “Prediction of Risk Score for Heart Disease in India using Machine Intelligence”,IPCSIT, Vol 4, 2011
Latha Parthiban and R.Subramanian, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm”, International Journal of Biological and Life Sciences, Vol 3(3), pp157-160,2007.
Liangxiao. J, Harry.Z, Zhihua.C and Jiang.S “One Dependency Augmented Naïve Bayes”, ADMA, pp 186-194, 2005
Huan Liu and Hiroshi Motoda, Rudy Setiono and Zheng Zhao. “Feature Selection: An Everlasting Frontier in Data Mining”, JMLR: The 4th Workshop on Feature Selection and Data Mining, 2010.
Rafiah Awang and Palaniappan. S “Web based Heart Disease Decision Support System using Data Mining Classification Modeling techniques” , Proceedings of iiWAS, pp 177-187, 2007
Carlos Ordonez, Edward Omincenski and Levien de Braal “Mining Constraint Association Rules to Predict Heart Disease”, Proceeding of 2001, IEEE International Conference of Data Mining, IEEE Computer Society, ISBN-0-7695-1119-8, 2001, pp: 433-440
Deepika. N, “Association Rule for Classification of Heart Attack patients”, IJAEST, Vol 11(2), pp 253-257, 2011.
Durairaj.M, and Meena.K” A Hybrid Prediction System using Rough Sets and Artificial Neural Network”, International Journal of Innovative Technology and Creative Engineering, Vol 1(7), July 2011.
Durairaj.M, Sivagowry.S. “A Survey on Particle Swarm Optimization and Rough Set Theory in Feature Selection for Heart Disease Prediction." International Journal of Computer Science and Mobile Computing (IJCSMC) Vol 4 (3) (2015): 87-92.
Sudha.A, Gayathri.p and Jaishankar. N “Utilization of Data Mining Approaches for prediction of life Threatening Disease Survivability”, IJAC (0975-8887), Vol 14(17), March 2012.
Rafiah Awang and Palaniappan. S “Intelligent Heart Disease Prediction System Using Data Mining techniques”, IJCSNS, Vol 8(8), pp 343-350, Aug 2008
Sivagowry.S, Dr. Durairaj. M. (2014). PSO-An Intellectual Technique for Feature Reduction on Heart Malady Anticipation Data. International journal of Advanced Research in Computer Science and Software Engineering, 4(9), 610-621.
Sivagowry.S, Dr. Durairaj.M. "A Pragmatic Approach of Preprocessing the Data Set for Heart Disease Prediction." International Journal of Innovative Research in Computer and Communication Engineering 2.11 (2014): 6457-6465.
World Health Organization. Strategic priorities of the WHO Cardiovascular Disease programme. Available online at URL: http://www.who.int/whr/200. Last accessed February 2006.
Chen A.H., “HDPS: Heart Disease Prediction System”, Computing in Cardiology, ISSN 0276-6574, pp 557-560, 2011.
en.wikipedia.org/wiki/myocardial_infarction
Nidhi Bhatia and Kiran Jyothi, “A Novel Approach for heart disease diagnosis using Data Mining and Fuzzy logic”, IJCA, Vol 54(17), pp 16-21, September 2012.
Sivagowry, S., M. Durairaj, and A. Persia. "An empirical study on applying data mining techniques for the analysis and prediction of heart disease." Information Communication and Embedded Systems (ICICES), 2013 International Conference on. IEEE, 2013.
Durairaj.M, Sivagowry.S. "Feature Diminution by Using Particle Swarm Optimization for Envisaging the Heart Syndrome." International Journal of Information Technology and Computer Science(IJITCS) (7).2 (2015): 35-43.
Muthukarupan S and Erib M.J, “ A hybrid PSO based fuzzy expert system for the diagnosis of Coronary heart system”, Expert systems with Application, Vol 39, pp 11657-11665, Elsevier, 2012
Neera Pathania and Rithika, “FIS Rule based Heart Disease Prediction System to predict the risk level of Heart Disease”, IJCEM, Vol 7, October 2014.
Saeed Ayat, Asieh Khosravanian, “Identification and classification of Coronary Artery disease patients using Neuro-Fuzzy Inference system”, Journal of Mathematics and Computer Science, Vol 13, pp 16-141, 2014.
Saravana kumar, Tholkapia arasu, “Rough Set Theory and Fuzzy Logic based warehousing of Heterogeneous clinical database”.
AVS Kumar, “ Generating rules for Advanced Fuzzy Resolution mechanism to diagnosis Heart Disease” , International Journal of Computer Application, Vol 77(11), pp 6-12, September 2012.
Mohd. A.M. Abushariah, Assal A.M Alquadah, Omar Y.Adwan, Rana M.M. Yousef, “ Automatic Heart Disease Diagnosis system based on Artificial Neural Network and ANFIS approach”, Journal of Software Engineering and Application, pp 1055-1064, 2014
UCI machine learning repository: http://archive.ics.uci.edu/ml/datasets/Heart+Disease: Last visited 8th August, 2014.
Durairaj.M, Sivagowry.S, “ An Intelligent Hybrid Quick Reduct Particle Swarm Optimization Algorithm for feature Reduction in Cardiac Disease Prediction”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), Volume 12(2), March-May 2015, pp.163-173
