<![CDATA[This research aims to compare the effectiveness of two diagnostic methods in identifying Von Hippel-Lindau (VHL) disease, namely the Bayes' Theorem and Certainty Factor. VHL is a rare disease that affects organs such as the brain, eyes, and kidneys. Health is a precious asset for humans, and often the assistance of specialized doctors is required to diagnose complex conditions like VHL. In the era of modern technology, the fusion of medical science with artificial intelligence has provided a fresh impetus in the development of expert systems to support faster and more accurate medical diagnoses. The Bayes' Theorem method is a statistical technique used to calculate the level of certainty in medical data. It helps measure the probability of whether someone has VHL or not. On the other hand, Certainty Factor is another method that gauges the level of confidence in diagnosing a disease using specific metrics, such as how certain symptoms are related to the disease. This research will conduct experiments on both methods using existing medical data and VHL cases. We will compare the accuracy, efficiency, and speed of both methods in diagnosing VHL. The results of this study are expected to provide valuable insights into which method is better suited to support VHL diagnosis. The implementation of expert systems in the field of medicine is crucial as it can assist doctors in making better decisions. The findings of this research can contribute to the development of more advanced and accurate medical expert systems, which, in turn, will enhance the care and prognosis of patients with VHL and other diseases. Thus, this research has the potential for significant impact in the fields of health and computer science. The results of the study indicate two methods in diagnosing Von Hippel-Lindau disease: "Certainty Factor" with a certainty level of 97.44%, and "Bayes' Theorem" with a certainty level of 41.22%. This provides insights into the relative effectiveness of both methods in diagnosing the disease, with "Certainty Factor" appearing to be more reliable.]]>
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