%0 Journal Article
	%A N.Poolsawad and  C.Kambhampati and  J. G. F. Cleland
	%D 2011
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 60, 2011
	%T Feature Selection Approaches with Missing Values Handling for Data Mining - A Case Study of Heart Failure Dataset
	%U https://publications.waset.org/pdf/15882
	%V 60
	%X In this paper, we investigated the characteristic of a
clinical dataseton the feature selection and classification
measurements which deal with missing values problem.And also
posed the appropriated techniques to achieve the aim of the activity;
in this research aims to find features that have high effect to mortality
and mortality time frame. We quantify the complexity of a clinical
dataset. According to the complexity of the dataset, we proposed the
data mining processto cope their complexity; missing values, high
dimensionality, and the prediction problem by using the methods of
missing value replacement, feature selection, and classification.The
experimental results will extend to develop the prediction model for
	%P 671 - 680