Abstract
Paget's Disease, also known as Paget's Disease of the bone, is a bone disorder that typically arises in the elderly, particularly after the age of 40. The risk of this disease increases with advancing age. Aging and genetic factors are believed to play a role in the development of this condition. Symptoms include bone pain, bone fragility, abnormal bone growth, changes in bone shape, decreased hearing, as well as symptoms such as headaches, dizziness, and joint complaints. Expert systems or artificial intelligence draw inspiration from the knowledge of experts to analyze situations. With algorithms like the Bayes theorem, this system provides solutions to emerging issues. In this context, expert systems aid doctors in identifying diseases without face-to-face consultations. The Bayes theorem serves as the foundation for this mechanism, emulating expert abilities. This research applies the Bayes theorem for an efficient and objective diagnosis of Paget's Disease. The results indicate a strong likelihood of patients having Paget's Disease at 73.87%. Consequently, the use of expert systems has the potential to enhance efficiency and objectivity in handling cases, assisting doctors in formulating diagnoses based on presenting symptoms.
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