<![CDATA[Anxiety disorders are a mental health problem that is increasing in prevalence, but the lack of public knowledge about anxiety disorders means that many people are not aware of anxiety disorders, so information technology is needed to understand symptoms and diagnosis using more relevant methods in intelligent systems. Intelligent systems are able to help in analyzing various symptoms and identifying initial diagnosis results with wider accessibility, but the problem of this research is focused on selecting the most effective method for intelligent systems as a basis for clinical data analysis, so in this research we will compare the level of accuracy of applying the method, namely Bayes' theorem and Certanty factor for the diagnosis of anxiety disorders. Bayes' Theorem is a classic statistical approach, offering a structured and measurable framework for calculating the probability of disease based on clinical evidence, while the Certainty Factor is a method for proving the certainty value of a fact in the form of a metric in an intelligent system. The aim of this research is to analyze the performance of the Bayes Theorem method and certainty factor by examining the percentage results obtained by applying the two methods. that the percentage result of the Bayes Theorem calculation method is higher, namely 84%, compared to the percentage result of the certainty factor, namely 70%, so it can be concluded that the application of Bayes' theorem is better than the certainty factor, especially in the diagnosis of people with anxiety disorders.]]>
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