A Methodology Based on Rough Set Theory and Hypergraph for the Prediction of Wart Treatment
Hossam A. Nabwey · International Journal of Engineering Research and Technology · 2020
Retrieving meaningful information from high dimensional dataset is an important and challenging task.Normally, medical dataset suffers from several issues such as curse of dimensionality problem, massive generation of highdimensional medical datasets from various biomedical applications, uncertainty, presence of missing values, nonrelevant and redundant attributes, etc.All of these issues harden the data analytic process for precise medical diagnosis.This work proposes an efficient feature selection methodology for finding the optimal feature subset which can be devised as a prominent solution to the above-said challenge.The proposed methodology based on rough set theory and hypergraph to identify the optimal feature subset for accurate prediction of wart treatment.In this work we use dataset contains information about wart treatment results of 90 patients using immunotherapy.A rough set with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts.After that the principles of hypergraph was applied to determine the minimal transversal of reducts.Finally, a set of generalized rules for wart treatment was extracted.The proposed model shows a higher efficiency in terms of reduct size, time complexity and overall accuracy rates as well as generates more compact rules.