RESUMO
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.
Assuntos
Sistemas Inteligentes , Análise Numérica Assistida por Computador , Processamento de Sinais Assistido por Computador , Incontinência Urinária/classificação , Incontinência Urinária/diagnóstico , Urodinâmica , Adulto , Idoso , Idoso de 80 Anos ou mais , Viés , Análise por Conglomerados , Diagnóstico Diferencial , Análise Discriminante , Feminino , Humanos , Modelos Logísticos , Pessoa de Meia-Idade , Análise Multivariada , Razão de Chances , Estudos Retrospectivos , Incontinência Urinária/fisiopatologiaRESUMO
Female urinary incontinence is a difficult problem for a patient but also for a physician. In the differential diagnosis of female urinary incontinence the physician has to determine a diagnostic class for the patient. This task is complex because of the unreliable patient history and the overlapping class boundaries. In order to develop an expert system to help the physician, a retrospective investigation on the incontinent women was performed to detect the potential expert system parameters. Also a diagnosis table was constructed from the expected values of parameters and the diagnostic classes. The results from K-means cluster analysis indicate that it is possible to develop the expert system on basis of the defined parameters and classes.
