Effect of Missing Data Imputation on Prediction of Urinary Incontinence Missing data imputation is an essential preprocessing step in clinical survey data mining applications. Rough set imputation is one way to handle missing data. A major advantage of using rough sets is that only the information presented in the dataset itself is sufficient to perform the analysis. Hence, no additional information, external parameters, models, functions, grades, or subjective interpretations are necessary. While there are several studies on rough set data imputation, none has been conducted to measure the effect of such imputation on prediction. In this paper, we generate several simulation datasets based on an existing epidemiological dataset (MESA) to perform such study. To measure how well each dataset lends itself to the prediction model, we have used pvalues from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and nonimputed simulation datasets were fit to the prediction model and they both turned out to be significant (pvalue u003c 0.05). In addition, the Wald score shows a better fit for the imputed compared to nonimputed datasets. The average confidence interval width was decreased by 10.4% when the imputed dataset was used, i.e. higher precision was achieved. The results show that using the rough set method for missing data imputation on MESA data improves the predictive capability. Further studies are required to generalize this conclusion to other clinical survey datasets.

Multiple Predictively Equivalent Risk Models for Handling Missing Data at Time of Prediction: with an Application in Severe Hypoglycemia Risk Prediction for Type 2 Diabetes Abstract   The presence of missing data at the time of prediction limits the application of risk models in clinical and research settings. Common ways of handling missing data at the time of prediction include measuring the missing value and employing statistical methods. Measuring missing value incurs additional cost, whereas previously reported statistical methods results in reduced performance compared to when all variables are measured. To tackle these challenges, we introduce a new strategy, the MMTOP algorithm (Multiple models for Missing values at Time Of Prediction), which does not require measuring additional data elements or data imputation. Specifically, at model construction time, the MMTOP constructs multiple predictively equivalent risk models utilizing different risk factor sets. The collection of models are stored and to be queried at prediction time. To predict an individualxe2x80x99s risk in the presence of incomplete data, the MMTOP selects the risk model based on measurement availability for that individual from the collection of predictively equivalent models and makes the risk prediction with the selected model. We illustrate the MMTOP with severe hypoglycemia (SH) risk prediction based on data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. We identified 77 predictively equivalent models for SH with crossvalidated cindex of 0.77xc2xb10.03. These models are based on 77 distinct risk factor sets containing 1217 risk factors. In terms of handling missing data at the time of prediction, the MMTOP outperforms all four tested competitor methods and maintains consistent performance as the number of missing variables increase.