A Rough Set Approach for Knowledge Acquisition on Vector-Borne Diseases

Rough Set Theory (RST) is considered to be one of the feasible and effective methodologies in the applications of database mining and found to be an important tool for knowledge discovery from relational datasets. RST and its variances, covering based RST (CRST) in particular offer their suitability to discover relational patterns even from the data sets having incomplete and inconsistent information. The rate of infection and persistent transmission of virus due to vector-borne diseases (VBD) in medical data analysis is high due to high mobility of infected host and uncertainty in the available clinical data sets. The objective of this research is to find the suitability of RST and CRST methodologies in VBD based clinical data sets and to explore knowledge from such data sets. The approach certainly explores the usage of RST in allied applications in medical/clinical datasets.