Thyroid Diagnosis based Technique on Rough Sets with Modified Similarity Relation

Because of the patient’s inconsistent data, uncertain Thyroid Disease dataset is appeared in the learning process: irrelevant, redundant, missing, and huge features. In this paper, Rough sets theory is used in data discretization for continuous attribute values, data reduction and rule induction. Also, Rough sets try to cluster the Thyroid relation attributes in the presence of missing attribute values and build the Modified Similarity Relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each rule. The discernibility matrix has been constructed to compute the minimal sets of reducts, which is used to extract the minimal sets of decision rules that describe similarity relations among rules. Thus, the rule associated strength is measured.

@article{Radwan2013,
  title     = {Thyroid Diagnosis based Technique on Rough Sets with Modified Similarity Relation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {10},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Elsayed Radwan and Adel M.A. Assiri},
  doi       = {10.14569/IJACSA.2013.041019},
  url       = {https://doi.org/10.14569/IJACSA.2013.041019}
}