A Fuzzy Rule Based Approach to Predict Risk Level of Heart Disease

Abstract

Health care domain systems globally face lots of difficulties because of the high amount of risk factors of heart diseases in peoples WHO 2013 To reduce risk improved knowledge based expert systems played an important role and has a contribution towards the development of the healthcare system for cardiovascular disease To make use of benefits of knowledge based system it is necessary for health organizations and users must need to know the fuzzy rule based expert system s integrity efficiency and deployments which are the open challenges of current fuzzy logic based medical systems In our proposed system we have designed a fuzzy rule based expert system and also by using data mining technique we have reduced the total number of attributes Our system mainly focuses on cardiovascular disease diagnosis and the dataset taken from UCI Machine Learning Repository We explored in the existing work The majority of the researcher s experimentation was made on 14 attributes out of 76 While in our system we took advantage of 6 attributes for system design In the preliminary stage UCI data participated in suggested system that will get outcomes The performance of the system matched with Neural Network and J48 Decision Tree Algorithm