A method for fetal assessment using data mining and machine learning

If a woman is pregnant, it is important for both her and her doctor/clinician to be aware if there are problems with the developing fetus. There are currently ways to discover problems using both noninvasive and invasive techniques. The University of Arkansas for Medical Sciences (UAMS) has recently developed a noninvasive system called the Squid Array for Reproductive Assessment (SARA) that can be used to gather fetal heartbeat data. This raw data, however, must then be analyzed by a human being to determine if there is a problem with a given fetus. In this paper, we propose a method to enable a computer to determine if a fetus is in a healthy or unhealthy state by the employment of a technique that will allow for rapid analysis using data mining.