Toward Development of PreVoid Alerting System for Nocturnal Enuresis Patients: A Fuzzy-Based Approach for Determining the Level of Liquid Encased in Urinary Bladder

This study aims to develop a machine-learning empowered technique to quantify to what extent an individual's bladder is filled by observing the filling-voiding pattern of a patient over a training period. In this experiment, a pulse-echo sonar element is used to generate ultrasound pulses while the probe surface is positioned perpendicular to the bladder's position. From the reflected echoes, four features which show sufficient sensitiveness and therefore could be modulated noticeably by different levels of liquid encased in the bladder, are extracted. The extracted features are then fed into a novel intelligent decision support system– known as FECOC – which is based on hybridization of fuzzy inference systems (FIS) and error correcting output codes (ECOC). The proposed scheme tends to achieve better results when examined in real case studies.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research