An Identification of Tuberculosis (Tb) Disease in Humans using Naïve Bayesian Method
Abstract: Tuberculosis (TB) is
a disease that can cause a death if not recognized or not treated properly. To
reduce the death rate of tuberculosis patients, the health experts need to
diagnose that disease as early as possible. Based on the main indication data,
laboratory test results and the rontgen
photo, Naïve Bayesian approach in data mining techniques could be optimized to
diagnose tuberculosis. Naïve Bayes classifiers predict class membership
probabilities with a class that has the highest probability value. The output
of the system is an identification Tuberculosis type of the patients. Testing
of the system using 237 data sample with variation of cross-validation in 3, 5,
7 and 9-fold cross validation gives an average accuracy 85,95%.
Author: Agustin Trihartati S.,
C. Kuntoro Adi
Journal Code: jptinformatikagg160037
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