The SAGA questionnaire is a useful tool for patient-centered discussions about the treatment and management of LUTS, including OAB, and assisting physicians in tracking progress and managing patient expectations during therapy.
This work develops a decision-supported system based on machine learning and scoring measures to discover the kind of female urinary incontinence (FUI) of a given patient. This system has two main branches. Each patient is characterized by a set of features (age, weight, number of childbirths, etc.). The first task consists of selecting the feature set which best defines each FUI class. This feature set is computed according to a some scoring measures. The patients characterized by the optimum feature set are then classified according to a Support Vector Machine classifier. The results are evaluated in terms of macroaccuracy, i.e. the mean of the percentages of correctly classified stress, mixed and sensory urge incontinence.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.