Neural Networks: An Application for Predicting Smear Negative Pulmonary Tuberculosis

Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis (PT) cases reported yearly. Rapid and accurate diagnosis of SNPT could provide lower morbidity and mortality, and case detection at a less contagious status. The main objective of this work is to evaluate a prediction model for diagnosis of SNPT, useful for outpatients who are attended in settings with limited resources. The data used for developing the proposed models werecomprised of 136 patients from health care units. They were referred to the University Hospital in Rio de Janeiro, Brazil, from March 2001 to September 2002, with clinical-radiological suspicion of SNPT. Only symptoms and physical signs were used for constructing the neural network (NN) modelling, which was able to correctly classify 77% of patients from a test sample. The achievements of the NN model suggest that mathematical modelling, developed for classifying SNPT cases could be a useful tool for optimizing application of more expensive tests, and to avoid costs of unnecessary anti-PT treatment. In addition, the main features extracted by the neural model are shown to agree with current analysis from experts in the field.