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