attribute and one output attribute with 554 rules. Besides, a comparative table is also presented, where proposed methodology is better than other methodology. According to the proposed methodology results, that the performance is highly successful and it is a promising tool for identification of a heart disease patient at an early stage. We have achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCI dataset.
FRBF : A Fuzzy Rule Based Framework for Heart Disease Diagnosis
Objetos digitales
attribute and one output attribute with 554 rules. Besides, a comparative table is also presented, where proposed methodology is better than other methodology. According to the proposed methodology results, that the performance is highly successful and it is a promising tool for identification of a heart disease patient at an early stage. We have achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCI dataset.