A novel approach for imputation of missing values for mining medical datasets


Imputation of missing attribute values in medical datasets for extracting hidden knowledge from medical datasets is an interesting research topic of interest which is very challenging. One cannot eliminate missing values in medical records. The reason may be because some tests may not been conducted as they are cost effective, values missed when conducting clinical trials, values may not have been recorded to name some of the reasons. Data mining researchers have been proposing various approaches to find and impute missing values. In this paper, we propose a novel imputation approach for fixing missing values. The approach is based on clustering concept and aims at dimensionality reduction of the records. This serves the need to use the same records of lower dimension to be used for clustering and classification of medical records to arrive at accurate decision prediction. The case study discussed shows that the missing values can be fixed and imputed efficiently by achieving dimensionality reduction. The proposed approach for imputation also achieved dimensionality reduction to perform efficient prediction of missing values.

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@article{UshaRani2015ANA, title={A novel approach for imputation of missing values for mining medical datasets}, author={Yelipe UshaRani and P. Sammulal}, journal={2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)}, year={2015}, pages={1-8} }