IJMTES – AN EFFICIENT FUZZY RULE BASED EXPERT SYSTEM TO FORECAST RISK LEVEL OF HEART DISEASE

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : AN EFFICIENT FUZZY RULE BASED EXPERT SYSTEM TO FORECAST RISK LEVEL OF HEART DISEASE

Author’s Name : S Radhimeenakshi | P Nandhini Priyaunnamed

Volume 03 Issue 11 2016

ISSN no:  2348-3121

Page no: 33-40

Abstract – In recent times, coronary heart ailment is a main motive of dejection and fatality. All around the international, deaths because of coronary heart ailment is increasing hastily than from every other sickness. The prediction of the probably headaches regarding coronary heart ailment nicely earlier is a very challenging task. To become aware of the probable headaches, many structures are made that uses scientific statistics units for identifications. A number of the structures expect coronary heart disorder based totally on hazard factors. Lots of visible danger factors which can be not unusual in heart disorder patients may be used efficiently for prognosis. system primarily based on threat elements allows not best medical examiners for prediction however additionally warn the sufferers in advance about the probably presence of heart disease. Those structures also are helpful to store money and time. With this motivation, in our proposed system, we have designed a fuzzy rule based professional machine and also by way of the use of records mining approach we have decreased the whole range of attributes. The proposed system mainly specializes in cardiovascular sickness prognosis and the dataset taken from UCI. Most of the people of the researcher’s experimentation changed into made on 14 attributes out of 76. While, in this proposed work we took advantage of 6 attributes for system layout. Within the preliminary stage UCI, information participated in advised device in an effort to get results. The overall performance of the proposed work matched with Neural Network and J48 Decision Tree Algorithm.  

Keywords— Heart Disease; Fuzzy Rule Based System; Fuzzy Reasoning; Data Mining

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