Breast malignancy is an affliction when breast containers evolve cruelly without expiring. These disastrous units set the clump and deadlock different sane cells to drive perfectly and therefore restrict the usual serving of the body. These lumps concede possibly favorable or diseased, contingent upon the part of the breast where tumor composition begun. It is very authoritative to pinpoint this ailment at boot camp so concerning get suitable medication timely in accordance with its kind and level with a view to help the affected person to vanquish the affliction and win the conflict of continuation. To help humans for the same, few computers advocated structures are pre-owned to investigate the ailment to grace the time of oncologist that in spotting the affliction that may be used in medicating to a greater extent and bestowing them the reward of survival. Still there are some loopholes in these structures because of which they are not fully utilized. One of major reason being is its fearful and expensive procedure to be undertaken by women for collection of data such biopsy and mammography. This research provides the solution of the above mentioned problem which aims to design fuzzy rule based framework to diagnose the disease using the data which could be collected from the blood sample using fearless blood analysis report. This research is enhancement of previous research done by authors on UCI’s Coimbra breast cancer dataset using fuzzy rules to diagnose the disease which achieved the accuracy, sensitivity and specificity as 90.3%, 87.3% and 95% respectively to increase the accuracy up to 93.2%, specificity upto 88.6% and sensitivity as 96.6% by reducing the rules up to 435.The expert system uses fuzzifier of Mamdani interface system to fuzzify the input values and defuzzifier that pins the output as benign or malignant. It is straightforward tactic of diagnosing the ailment with cheering accuracy as a way to appeal the women with signs and symptoms to get it recognized at early levels thus help in lowering the mortality prices to a big fag give up.