  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 to
increase classification accuracies so that disease may be predicted accurately.
In this paper, we propose a novel imputation approach for imputation of missing
values and performing classification after fixing missing values. The approach
is based on clustering concept and aims at dimensionality reduction of the
records. The case study discussed shows that missing values can be fixed and
imputed efficiently by achieving dimensionality reduction. The importance of
proposed approach for classification is visible in the case study which assigns
single class label in contrary to multi-label assignment if dimensionality
reduction is not performed.
