Coping with Missing Values in Medical Data Sets: A Proposal of an Approach Based on Soft Clustering and Measures of Central Tendency

Medical data presents several hidden challenges compared to conventional datasets, the first challenge we encounter before even considering data processing and analysis is the presence of missing values in datasets. In this paper, we present a theoretical framework for missing values imputation through a fuzzy c-means machine learning clustering algorithm and statistical measures of central tendency, namely mean, median and mode. The algorithm of the proposed approach along with the experimental design is presented, aiming to evaluate the performance of the proposed missing values imputation on different medical datasets from the University of California Irvine Machine Learning Repository.