Abstract:
In many application domains, classification of
complex measurements is essential in a diagnostic
process. Correct classification of measurements may
in fact be the most critical part of the diagnostic
process. The main feature of the proposed system is
to provide a sample and integrated tool for
designing diagnostic application. Lung cancer is the
second most common malignancy in men and the
third most common cancer in women. Usually lung
cancer nodules have a multifocal origin and a
rather poor prognosis. Therefore, a careful review
of the symptoms presented and a detailed physical
exam greatly help with the diagnosis occurs. This
paper proposes a decision support system for lung
cancer classification using Bayesian Analysis to
help the physician or the patient who tests herself at
home for lung cancer with the most possible result.
Bayesian classification is one of the classification
methods successfully applied to the cancer
diagnostic problems. The system stores the
knowledge of the medical experts and the medical
records of the previous cases. Based on the
knowledge stored, the system will learn the patterns
using Bayesian Analysis and decide the probability
based on the symptoms of the patients. Classifier
accuracy is also estimated to get the better decision
support system with the minimum error rate by
using Bayesian Analysis that provides a theoretical
justification and lower error rate than other
classifiers, [6].
