Abstract
BackgroundPelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women.MethodsThe available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression.ResultsOf the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.
Highlights
Machine learning (ML) methods have the ability to learn about a system’s behavior directly from its observed data and do not require previous knowledge on the mathematical relations ruling it
The highest precision was 77.8%, which was obtained by the best configuration of the logistic regression (LR) with recursive feature elimination algorithm (RFE), and the highest recall was 58.3%, which was obtained by the best configuration out of the three classifiers without feature subset search (FSS), LR without FSS, and LR with RFE (Table 1)
The highest precision was 88.9%, which was obtained by the best configuration of the k-nearest neighbors (k-NN) with RFE, and the highest recall was 79.2%, which was obtained by the best configuration of the LR with RFE (Table 1)
Summary
Machine learning (ML) methods have the ability to learn about a system’s behavior directly from its observed data and do not require previous knowledge on the mathematical relations ruling it. In the biomedical engineering and clinical applications field, these methods have been increasingly employed in the construction of computer-aided diagnosis (CAD) systems. Such systems aim to reduce diagnostic dependence on professionals’ experience and the variability and subjectivity of the results (Mumtaz et al, 2017; Kao & Wei, 2011). Pelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. The best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, reaching 95.8% accuracy. Not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence
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