Abstract
Fuzzy modeling techniques have been widely used to solve the uncertainty problems. A diagnosis of coronary heart disease (CHD) consists of some parameters numerical value of lingustics data. It can be implemented using fuzzy system through construction of the rules which relate to the data. However, the range of linguistics value is determined by an expert that depends on his knowledge to interpret the problem. Therefore, we propose to generate the rules automatically from the data collection using subtractive clustering and fuzzy inference Tagaki Sugeno Kang orde-1 method. The subtractive clustering method is a clustering algorithm to look for data clusters that serve as the fuzzy rules for diagnosis of CHD risk. The selected cluster number is determined based on the value of variant boundaries. Hence, it is applied to fuzzy inference system method, Takagi Sugeno Kang order-1, which determines diagnnosis of the desease. The advantage of this method is applicable to generate the fuzzy rules without defining and describing from an expert.
Highlights
Fuzzy logic can be used to model the process of thinking human involves elements of uncertainty, doubt and linguistics
The subtractive clustering method is a clustering algorithm to look for data clusters that serve as the fuzzy rules for diagnosis of coronary heart disease (CHD) risk
One of the problems which can be solved in fuzzy system is diagnosis of Coronary Heart disease (CHD) due to the constructing rules of diagnosis are numerical data that have linguistic value
Summary
Fuzzy logic can be used to model the process of thinking human involves elements of uncertainty, doubt and linguistics. The determination of fuzzy rules has been established and elaborated by experts This process takes a long time, experience and the ability of experts [2]. The strenghhness of fuzzy clustering is the computation time efficiently [4] It needs to establish the fuzzy rules, which are automatically based on optimizing data input and output system [5]. One of the problems which can be solved in fuzzy system is diagnosis of Coronary Heart disease (CHD) due to the constructing rules of diagnosis are numerical data that have linguistic value. We propose to apply subtractive clustering to generate the fuzzy rule for CHD risk
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