Improving Performance Fuzzy Methods in Stroke Disease Diagnosis Systems with Certainty Factor

Stroke is a disease caused by damage to brain cells due to impaired blood supply. Stroke has causes such as hypertension, cholesterol, arteriosclerosis, heart disease, diabetes, family history of stroke, and migraine. The purpose of this study is to develop a stroke diagnosis system by improving the performance of the fuzzy method with the performance of the Certainty Factor (CF) method. The resulting diagnosis system based on the performance of the fuzzy method enhanced with CF can present an effective and efficient diagnosis system in decision-making. The research dataset refers to the knowledge processed about stroke symptoms obtained from an expert. The results of testing the performance of the fuzzy method enhanced with CF in the diagnosis process have been able to provide the knowledge needed by patients in preventing stroke. The performance of fuzzy in the diagnosis system has also made predictions or early diagnoses so that solutions or ways to treat stroke are obtained optimally. Improving the performance of the fuzzy method with CF can present an effective and efficient system for diagnosing stroke. The accuracy of the diagnosis system produced by the fuzzy method based on testing presents results of $\mathbf{8 9 \%}$ in diagnosing stroke. The results of improving the performance of the fuzzy method with CF have also provided innovation in the Knowledge-Based System in the stroke diagnosis process. Based on these results, this study can contribute to helping medical personnel in providing alternative solutions for early prevention of increasing stroke incidence.