Applying Rough Set Theory for Medical Informatics Data Analysis
Keywords:
Rough Sets Theory, Medical Data Analysis, ROSETTA tool kit, in-vitro FertilizationAbstract
In the medical field each and every data is important, because these data are very essential for human life. Medical data analysis is a very big and complex task. The medical data consists of imprecise, (or) uncertainty, (or) incomplete data. Therefore the medical data analysis process requires excellent techniques for processing, storing and accessing the datasets. Some of the traditional techniques are available to process the incomplete data and these techniques requires additional information to process the imprecise dataset. In this paper, we propose an intelligent technique of rough set theory for analyzing the imprecise medical data, which could be used for extracting knowledge without changing the knowledge of the original. In comparison to traditional techniques, rough set theory gives the optimal result from the analysis process without loss of information. ROSETTA is a toolkit for analyzing tabular data within the framework of rough set theory that could be applied in the original dataset to compute the reduced set without the loss of the knowledge of the original set. In this paper, the medical data set of recorded information from IVF (in-vitro fertilization) tests are used for data analysis, in which the influential parameters (tests) are identified using Rough Set Theory. The identified influential parameters display the determining impact on the result of IVF treatment (Test tube baby treatment). ROSETTA toolkit used to predict the influential parameters in the IVF treatment.
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