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Missing data in databases are considered to be one of the biggest problems faced on data mining application. This problem can be aggravated when there is massive missing data in the presence of imbalanced databases. Several techniques as samples deletion, values imputation, values prediction through classifiers and approximation of patterns have been proposed and compared, but these comparisons do not consider adverse conditions found in real databases. In this work, it is presented a comparison of techniques used to classify records from a real imbalanced database with massive missing data, where the main objective is the database pre-processing to recover and select records completely filled for further techniques application. It was compared with other algorithms such as clustering, decision tree, artificial neural networks and Bayesian classifier, expressing their efficiency through ROC curves. Through the results, it can be verified that the problem characterization and database understanding are essential steps for a correct techniques comparison in a real problem. It was observed that artificial neural networks are an interesting alternative for this kind of problem since it was capable to obtain satisfactory results even when dealing with real-world problems.