An Enhanced Weighted Associative Classification Algorithm without Preassigned Weight based on Ranking Hubs

Heart disease is the preeminent reasons for death worldwide and in excess of 17 million individuals were kicked the bucket from heart disease in the past years and the mortality rate will be increased in upcoming years revealed by WHO. It is very tough to diagnose the heart problem by just observing the patient. There is a high demand in developing an efficient classifier model to help the physician to predict such threatening disease to recover the human life. Now a day, many researchers have focused novel classifier model based on Associative Classification (AC). But most of the AC algorithm does not consider the consequence of the attribute in the database and treat every itemsets equally. Moreover, weighted AC ignores the significance of the itemsets and suffering the rule evaluation due to support measure. In this proposed method we have introduced attribute weight, which does not require manual assignment of weight instead the weight would be calculated from link based model. Finally, the performance of the proposed algorithm is verified on different medical datasets from UCI repository with classical associative classification.

@article{P2019,
  title     = {An Enhanced Weighted Associative Classification Algorithm without Preassigned Weight based on Ranking Hubs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {10},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Siddique Ibrahim S P and Sivabalakrishnan M},
  doi       = {10.14569/IJACSA.2019.0101041},
  url       = {https://doi.org/10.14569/IJACSA.2019.0101041}
}