TITLE:
Fuzzy Rule Generation for Diagnosis of Coronary Heart Disease Risk Using Substractive Clustering Method
AUTHORS:
Lailil Muflikhah, Yeni Wahyuningsih, Marji
KEYWORDS:
Fuzzy Rule; Coronary Heart Disease; Subtractive Clustering; Takagi Sugeno Kang; Variant
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.6 No.7,
July
9,
2013
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.