An efficient approach for imputation and classification of medical data values using class-based clustering of medical records

Medical data is usually not free from missing values and this is also true when data is collected and sampled through various clinical trials. Existing Imputation techniques do not address the problem of high dimensionality and apply distance functions that also have the curse of high dimensionality. There is a need to turn up with innovative approaches and methods for accurate and efficient analysis of medical records. This research proposes an improved imputation approach called IM-CBC (Imputation based on class-based clustering) and a classifier termed as the Class-Based-Clustering Classifier(CBCC-IM). Experiments are performed on nine benchmark datasets and the recorded results using IM-CBC imputation approach are compared to ten imputation approaches using classifiers KNN, SVM and C4.5 and to the CBCC classifier using Euclidean distance and fuzzy gaussian similarity functions. Results obtained prove that the performance of classifiers is improved or atleast nearer to the existing approaches. CBCC-IM classifier records highest accuracy when compared to all other classifiers on benchmark datasets such as Cleveland, Ecoli, Iris, Pima, Wine and Wisconsin.