Applicability of Data Mining Technique Using Bayesians Network in Diagnosis of Genetic Diseases

This study aims to identify a methodology to aid in the identification of diagnosis for chromosomal abnormalities and genetic diseases, presenting as a tutorial model the Turner Syndrome. So, it has been used classification techniques based in decision trees, probabilistic networks (Naïve Bayes, TAN e BAN) and neural MLP network (Multi-Layer Perception) and training algorithm by error retro-propagation. Described tools capable of propagating evidence and developing techniques of generating efficient inference techniques to combine expert knowledge with data defined in a database. We have come to a conclusion about the best solution to work out the show problem in this study that was the Naïve Bayes model, because this presented the greatest accuracy. The decision - ID3, TAN e BAN tree models presented solutions to the indicated problem, but those were not as much satisfactory as the Naïve Bayes. However, the neural network did not promote a satisfactory solution.

@article{Filho2013,
  title     = {Applicability of Data Mining Technique Using Bayesians Network in Diagnosis of Genetic Diseases},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {1},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Hugo Pereira Leite Filho},
  doi       = {10.14569/IJACSA.2013.040107},
  url       = {https://doi.org/10.14569/IJACSA.2013.040107}
}