Using rough set theory for mining the level of hearing loss diagnosis knowledge

This paper focused on the development of diagnosis knowledge model of the level of hearing loss in the audiology clinic patients using rough set theory. A knowledge model contains a set of knowledge via rules that are obtained from mining certain amount of data. These data consist of valuable knowledge that impossible for the audiologist or audio therapist to extract without powerful mining techniques or tools. These rules help doctors in decision making such as setting up new strategy to improve the efficiency of the operation. In this work, a data mining technique, rough set theory was used for the knowledge modelling. It was used based on its capability of handling uncertain data that often occurs in real world problems. The results from the modelling produced a classifier called rough classifier. The classifier was used to classify the level of hearing loss. A total of 500 data obtained from the audiology clinic. The data consisted of 24 attributes from four categories namely demography, antenatal, neonatal and medical categories. These attributes were used as an input and one attribute called diagnostic category as an output. In order to facilitate the modelling process requirement, these attributes have been gone a pre-process stage. The best model has been obtained from 10 experiments using 10 sets of different training and test data. The experiment showed promising results with 76% accuracy. The developed knowledge model has a great potential to be embedded in the development of the medical decision support system.