@article{(Open Science Index):https://publications.waset.org/pdf/15882,
	  title     = {Feature Selection Approaches with Missing Values Handling for Data Mining - A Case Study of Heart Failure Dataset},
	  author    = {N.Poolsawad and  C.Kambhampati and  J. G. F. Cleland},
	  country	= {},
	  institution	= {},
	  abstract     = {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
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {5},
	  number    = {12},
	  year      = {2011},
	  pages     = {671 - 680},
	  ee        = {https://publications.waset.org/pdf/15882},
	  url   	= {https://publications.waset.org/vol/60},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 60, 2011},