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Case-Based Reasoning produces a solution based on similarities to previous cases. New case solutions result from the placement of similarities with old cases. In this reseach the authors applied CBR to diagnose tuberculosis. System knowledge sources are obtained by collecting medical records of tuberculosis patients in 2014-2016. Calculation of similarity values using the K-Nearest Neighbor algorithm with a thereshold value of 80%. This system can diagnose 3 types of tuberculosis based on 25 symptoms. The system output consists of the type of tuberculosis based on the symptoms experienced by the patient, treatment solutions and presentation of similarities between new cases and old cases. Based on the results of testing with 51 cases the results: (a) testing with 3 new case scenarios obtained the accuracy of each system for data scenarios obtained by 31 training data (60% of 51 cases) and 20 test data (40% of 51 cases) accuracy is 63%, the second scenario accuracy obtained with 35 training data (70% of 51 cases) and 16 test data (30% of 51 cases) accuracy is 69.2% and the third scenario accuracy obtained with 41 training data (80% of 51 cases) and 10 test data (20% of 51 cases) accuracy is 90%. (b) The results of testing of the old cases in the case base obtained 100% accuracy of the system.
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