Mantra

Medical record instances can have missing values which makes them unsuitable for learning process. Data Imputation is normally done to fill one or more missing data attribute values. Imputation helps to perform supervised or un-supervised learning after the dataset is free from missing data. Learning process helps the discovery of hidden, valuable and important information that can provide insightful results. Imputation is a data pre-processing task that requires applying distance function to find missing values. In this paper, a distance function named MANTRA is proposed to impute missing data values. The distance function is also called as the imputation measure since it is designed for imputation of missing values. A working example is demonstrated that shows how imputation is achieved using proposed distance function, MANTRA. It is proved that the nominal value that is filled after imputation is same as the original.