A Fuzzy Rule Based Approach To Predict Risk Level Of Heart Disease

In the21st century, healthcare systems worldwide face many challenges due to the growing burden of cardiovascular disease in elderly’s, kids and younger peoples (W.H.O,2013). The incredible development in information and communication technology during the last decades has instigated an extraordinary dependence on ICT systems. ICT systems are at the essence of modern medical productivity sources, medical organizational forms, and construction of a global economy. Fuzzy rules based systems play a substantial role in ICT and have a contribution to the development of the healthcare system for cardiovascular disease, which is the underpinning of recent medical, social and economic development strategies. However, in order to take advantages of fuzzy rules, it is necessary that the medical enteiprises and patients should belief the fuzzy rules based diagnosis system in terms of reliability, performance, and deployments, which are the open challenges of current Fuzzy Logic based medical systems. In the existing literature, many researchers and academicians were working on fuzzy logic based decision support systems, intelligent systems, robotic systems, medical data processing, and analyzing systems for diagnosis of heterogeneous diseases in the healthcare domain. But in these fuzzy rules based decision support system for cardiovascular disease were less discussed and required further more research. While, our proposed fuzzy rule based diagnosis system for cardiovascular patients is more effective and efficient.In our proposed system, we minimized the number of input attributes that will absolutely minimize the number of diagnostic tests. We used seven (7) attributes, six (6) inputs, and one (1) output result, which recognize the cardiovascular disease in patients. We enter six vital signs/symptoms/attributes into computer as a numerical data inputs such as (Age, blood pressure, Cholesterol, Maximum heart rate, Old peak, Thallium scan) which indicate the input of fuzzy logic and the outputs will be a range of the risk from0to1, such as Healthy, Sick.In our proposed system, we used Mamdani inference method. Our system mainly focuses on cardiovascular disease diagnosis, and the dataset took from UCI. UCI database consists of76several attributes; we investigated in the existing work. Most of the experiments were made by using a subset of14from UCI. While, in our system we used seven attributes for experiment. From seven attributes, we used six attributes as input, and one attribute for output. In the first stage, a doctor checks the patient, and then taking patient symptoms such as age, sex, and heart rate. In the second stage, numerical data will enter into the proposed system, and in the last; system will acquire results. The main purpose of our research is tantamount to design and implement fuzzy rules based system for cardiovascular patients.