Evaluating Imputation Techniques for Missing Data in ADNI: A Patient Classification Study

In real-world applications it is common to find data sets whose records contain missing values. As many data analysis algorithms are not designed to work with missing data, all variables associated with such records are generally removed from the analysis. A better alternative is to employ data imputation techniques to estimate the missing values using statistical relationships among the variables. In this work, we test the most common imputation methods used in the literature for filling missing records in the ADNI (Alzheimer's Disease Neuroimaging Initiative) data set, which affects about 80% of the patients-making unwise the removal of most of the data. We measure the imputation error of the different techniques and then evaluate their impact on classification performance. We train support vector machine and random forest classifiers using all the imputed data as opposed to a reduced set of samples having complete records, for the task of discriminating among different stages of the Alzheimer's disease. Our results show the importance of using imputation procedures to achieve higher accuracy and robustness in the classification.