A robust approach for classifying unknown data in medical diagnosis problems

Unknown data is a common drawback in medical diagnosis applications. A recommended procedure for dealing with unknown values is missing data imputation, i.e., estimating and filling missing values using all the available information. This work* presents a robust approach for incomplete data classification using an enhanced version of the K Nearest Neighbours algorithm. Experimental results on medical diagnosis databases show the usefulness of this approach.