A Knowledge Acquisition Model of Inconsistent Medical Data Based on Rough Sets Theory

Knowledge acquisition includes the elicitation, collection, analysis, modeling and validation of knowledge for knowledge engineering and knowledge management projects. It is very valuable and has great development prospects to apply various knowledge acquisition techniques in medical data to explore the interrelations and laws between various diseases, sum up the medical effects of various treatment schemes and carry on diagnosis, treatment and medical research. However, the incomplete and inconsistent medical data make many knowledge acquisition methods ineffective. Rough sets theory is a mathematical tool for extracting knowledge from uncertain and incomplete information. The paper tries to apply the reduction of rough sets to remove redundant medical data and access to real and effective medical knowledge for finding the rules and models of medical diagnosis.