Missing attribute values are quite common in the datasets available in the literature. Missing values are also possible because all attributes values may not be recorded and hence unavailable due to several practical reasons. For all these one must fix missing attribute vales if the analysis has to be done. Imputation is the first step in analyzing medical datasets. Hence this has achieved significant contribution from several medical domain researchers. Several data mining researchers have proposed various methods and approaches to impute missing values. However very few of them concentrate on dimensionality reduction. In this paper, we discuss a novel imputation framework for missing values imputation. Our approach of filling missing values is rooted on class based clustering approach and essentially aims at medical records dimensionality reduction. We use these dimensionality records for carrying prediction and classification analysis. A case study is discussed which shows how imputation is performed using proposed method.