Abstract:
Tuberculosis (TB) is a social disease with
medical aspects. By the increasing availability of biomedical
and health-care data with a wide range of
characteristics, computer-based medical system is
playing an increasingly relevant role in assisting both
diagnosis and treatment. Base on the knowledge
stored, the system will learn the patterns using Naive
Bayesian classification and decides the category of TB
by probabilities. It is based on the theorem of posterior
probability. This system intends to develop a diagnosis
system of automatic classification method for TB
diagnosis based on the symptoms of the patients. The
system stores the knowledge of the medical experts
and the medical records of the previous case as
Training database. This system also considers the
missing value by filling data completely, because it
needs the actual symptoms of the patient for
increasing accuracy to classify. This system can give
the category of TB and treatment for the patient who
has TB symptoms by using Naive Bayesian
classification method on the Training database. The
accuracy of the system for that patient is shown by
using hold-out method on testing database.
