Skip pattern analysis for stratification and detection of undetermined and inconsistent data

Urinary Incontinence (UI) is a costly condition that decreases the quality of a patient's life and social engagement. Identification of UI risk factors may help early prevention and treatment of the condition. In this study we revisited the Medical, Epidemiological and Social Aspects of Aging (MESA) data collected in 1983 by the University of Michigan. The experiments are conducted on the dataset pertaining to the female-only population. The dataset contains missing values. First, the missing values are classified into inconsistent, undetermined, genuine missing values and skip patterns. The undetermined and inconsistent values are distinguished from the skip patterns and removed from the dataset. Once the skip patterns are detected, they are used to stratify the MESA data. Based on the stratification performed, the important risk factors are then analyzed for each group of subjects. JRip rule extraction technique is utilized to determine the UI risk factors. Consequently, taking female hormones was determined as the most important stratifying feature. The dataset is then stratified to two subsets based on this stratifying feature. Education level, hearing problems, urine loss while coughing or sneezing, physical activity, stress and cancer are risk factors specific to taking female hormones. The common risk factors among both of the stratified groups were: stress, frequent sneezing, and low physical activity. Although there were common risk factors among both of the stratified groups these preliminary results show that different group of subjects have different risk factors, and therefore they should be provided with different diagnoses and possibly treatment plans.