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Abstract
In recent years, Artificial Intelligence methods have
largely been apllied in various fields including medical
applications. Breast cancer is the cause of death worldwide,
especially among women. To make a diagnosis of breast cancer, a
doctor had difficulty in determining the risk status of breast cancer.
This study aims to develop a decision support system (DSS) based
fuzzy to help doctors classify the risk of breast cancer. The system
has six input variables (Her2, hormone receptors, age, tumor grade,
tumor size, lymph node and) and one output variable (risk status).
This system uses a model inference Mamdani (max), where each
value of each rule generated from several tNorm implications
method such as tNorm Zadeh, tNorm Dombi, tNorm DuboisPrade,
tNorm Yager, tNorm Product, and tNorm EinsteinProduct.
Defuzzyfication method are used is centroid method. Based on the
tests performed, Risk Status value obtained is 2.7089 for tZadeh,
tDombi, tYager and tProduct. Risk Status for tDubois Prade is 2.86,
while for tEinstain Product, Risk status is 3.35. In further research,
this system can be applied to other types of cancer or using other
inference methods for the preferences of the previous case.
largely been apllied in various fields including medical
applications. Breast cancer is the cause of death worldwide,
especially among women. To make a diagnosis of breast cancer, a
doctor had difficulty in determining the risk status of breast cancer.
This study aims to develop a decision support system (DSS) based
fuzzy to help doctors classify the risk of breast cancer. The system
has six input variables (Her2, hormone receptors, age, tumor grade,
tumor size, lymph node and) and one output variable (risk status).
This system uses a model inference Mamdani (max), where each
value of each rule generated from several tNorm implications
method such as tNorm Zadeh, tNorm Dombi, tNorm DuboisPrade,
tNorm Yager, tNorm Product, and tNorm EinsteinProduct.
Defuzzyfication method are used is centroid method. Based on the
tests performed, Risk Status value obtained is 2.7089 for tZadeh,
tDombi, tYager and tProduct. Risk Status for tDubois Prade is 2.86,
while for tEinstain Product, Risk status is 3.35. In further research,
this system can be applied to other types of cancer or using other
inference methods for the preferences of the previous case.