An improved feature reduction approach based on redundancy techniques for predicting female urinary incontinence

Abstract

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.

Applications of computational mathematics in science and engineering

“…In this sense, we have selected an article that uses machine learning techniques to predict a determined health problem in humans [10], and a second one that makes use of pattern analysis to handle with of database of leukaemia patients [9].…”