Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm

J Med Syst. 2011 Jun;35(3):329-32. doi: 10.1007/s10916-009-9369-3. Epub 2009 Aug 28.

Abstract

Tuberculosis is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly Mycobacterium tuberculosis (Wikipedia 2009). It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all over the world, and in Turkey as well. This article presents a study on tuberculosis diagnosis, carried out with the help of multilayer neural networks (MLNNs). For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used. The tuberculosis dataset was taken from a state hospital's database, based on patient's epicrisis reports.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms*
  • Databases, Factual
  • Decision Support Systems, Clinical
  • Genetic Phenomena
  • Humans
  • Neural Networks, Computer*
  • Reproducibility of Results
  • Tuberculosis / diagnosis*
  • Tuberculosis / genetics
  • Turkey