<![CDATA[This research aims to conduct a comparative analysis between two diagnostic methods, namely Bayes' Theorem and Case Based Reasoning, in detecting Polymyalgia Rheumatica. This disease is known for its symptoms of muscle pain and stiffness in certain parts of the body, so correct diagnosis is very important for effective disease management. The problem at hand is increasing the accuracy and effectiveness of diagnosis, and this research details how both methods are applied. The Bayes Theorem method is used to calculate the probability of the existence of a disease based on the symptoms that appear. Meanwhile, Case Based Reasoning utilizes knowledge from previous cases to determine the diagnosis in new cases. Data collection involves information on Polymyalgia Rheumatica symptoms from a number of patients. The comparative analysis includes evaluation of the accuracy, sensitivity, specificity, and computational time of both methods. It is hoped that the results of this research will provide a deeper understanding of the effectiveness of each method in supporting the diagnosis of Polymyalgia Rheumatica. These findings are expected to make an important contribution to the development of better diagnostic systems, with the potential to improve medical practitioners' ability to identify and manage this disease more efficiently. The results of the analysis using two approaches, namely Bayes' Theorem and Case-Based Reasoning, to assess Polymyalgia Rheumatica, revealed quite striking differences in the level of prediction certainty. Bayes' theorem sets the probability at around 22.1%, while the Case-Based Reasoning approach gives a probability level of up to 52%.]]>
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