Feature selection technique attempts to select and remove irrelevant
features while ensuring that an informative subset of features remains
in the dataset. The performance of a classifier often depends on the
feature subset used for the robust classification task. In the medical and
healthcare application domain, classification accuracy plays a vital
role. The higher level of false negatives in medical diagnosis systems
may raise the risk of patients not employing the necessary treatment
they need. In this article, we have proposed an unsupervised feature
selection method that underlines the concepts of rough set theory for
the task of classification of high-dimensional datasets. Experiments are
carried out on seven public domain healthcare and life science related
datasets. The obtained experimental results justify the significance of
the proposed method over five other state-of-the-art feature selection
methods.