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This paper presents an impact assessment for the imputation of missing data.The data set used is HIV Seroprevalence data from an antenatal clinic studysurvey performed in 2001. Data imputation is performed through five methods:Random Forests, Autoassociative Neural Networks with Genetic Algorithms,Autoassociative Neuro-Fuzzy configurations, and two Random Forest and NeuralNetwork based hybrids. Results indicate that Random Forests are superior inimputing missing data in terms both of accuracy and of computation time, withaccuracy increases of up to 32% on average for certain variables when comparedwith autoassociative networks. While the hybrid systems have significantpromise, they are hindered by their Neural Network components. The imputed datais used to test for impact in three ways: through statistical analysis, HIVstatus classification and through probability prediction with LogisticRegression. Results indicate that these methods are fairly immune to imputeddata, and that the impact is not highly significant, with linear correlationsof 96% between HIV probability prediction and a set of two imputed variablesusing the logistic regression analysis.

Evaluating the Impact of Missing Data Imputation through the use of the Random Forest Algorithm