Dimensionality Reduction using Rough Sets - A Framework
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Date
2013
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Abstract
The
healthcare organizations are marching towards improving the quality of services and the
efficacy of patients’ care by adopting data mining technologies. In an effort to turn information into
knowledge, they face a lot of challenges when trying to deal with large, diverse, and often-complex healthcare
data sources. In the presence of hundreds or thousands of features it is found that only very few of the
features predominantly contribute for the medical decision making. The objective of this research work is to
prove that a small subset of informative features, selected from a whole set of features, may carry enough
information to construct reasonably accurate prognostic models. The focus is on selecting the optimal feature
subset, with informative and discriminatory features, required for quality medical diagnosis. The rough set
based methods are to be used to find the optimal feature subset of the medical databases that could enhance
the Accuracy, the Sensitivity and the Specificity of the classification algorithms.