Data from records of 99 patients were used to classify cardiac patients as to whether they were likely or unlikely to experience a subsequent morbid event after admission to a hospital. Both a linear discriminant function and a logistic regression equation were developed using a set of nine predictor variables that were chosen on the basis of their correlations with the likelihood of a subsequent morbid event. Once the models were obtained, artificially-generated missing values were replaced with imputed values using mean substitution, regression imputation, and hot-deck imputation techniques. The effect on the accuracy of the predictions using models with imputed values was determined by comparing the reclassifications using imputed data with the actual occurrence or nonoccurrence of a subsequent morbid event. Mean substitution and hot-deck imputation performed slightly better than regression imputation in this application regardless of whether or not the predictor variable whose values were being imputed was categorical or numerical. (Contains 2 tables and 18 references.) (Author/SLD)