Analysis of the imputed female urinary incontinence data for the evaluation of expert system parameters

We evaluated parameters for an expert system which will be designed to aid the differential diagnosis of female urinary incontinence by using knowledge discovered from data. To allow the statistical analysis, we applied means, regression and Expectation-Maximization (EM) imputation methods to fill in missing values. In addition, complete-case analysis was performed. Logistic regression results from the imputed data were reasonable. The significant parameters were mostly those that are important in the diagnostic work-up. Moreover, directions of relations between the parameters and the stress, mixed and sensory urge diagnoses were as expected. Analysis with the complete reduced data set gave clearly insufficient results. Imputed values had a moderate agreement, but odds ratios and classification accuracies of logistic regression equations were similar. Results suggest that with these data, simpler methods may be used to allow multivariate analysis and knowledge discovery, when better methods, such as EM imputation, are unavailable. Cluster analysis detected clusters corresponding to the small normal class, but was unable to clearly separate the larger incontinence classes.