Variable optimisation of medical image data by the learning Bayesian Network reasoning

The method proposed here uses Bayesian non-linear classifier to select optimal subset of attributes to avoid redundant variables and reduce data uncertainty in the classification process often used in medical diagnosis. The method also exploits the structural reasoning ability of Bayesian Networks (BN) to optimize large number of attributes to prevent overfitting, meanwhile it maintains the high classification accuracy. This process simplifies the complex data analyses and may lead to a cost reduction in clinical data acquisition process.