A framework for diagnosis of urinary incontinence disease based on scoring measures and automatic classifiers
This work develops a decision support system based on machine learning and scoring measures to determine the type of urinary incontinence in women with low urinary tract symptoms. This system has two main branches. The former consists of selecting the feature set which best defines the UI type from the set of features (age, weight, etc.) characterizing a patient. This feature set is computed from several scoring measures. The patients characterized by the optimum feature set are then classified according to C4.5 and SVM classifiers. The results are evaluated according to Sensitivity and Specificity evaluation measures. The management of the final system is simple and its performance is high, getting Sensitivities over 80% and Specificities near 100% for some configurations.
diagnosis urinary incontinence scoring measures machine learning feature selection