Title :
A method for fetal assessment using data mining and machine learning
Author :
Copeland, W. ; Chia-Chu Chiang
Author_Institution :
Dept. of Comput. Sci., Univ. of Arkansas at Little Rock, Little Rock, AR, USA
Abstract :
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.
Keywords :
bioinformatics; data mining; learning (artificial intelligence); medical diagnostic computing; obstetrics; SARA; UAMS; University of Arkansas for Medical Sciences; bioinformatics; data mining; developing fetus; fetal assessment; fetal heartbeat data; invasive techniques; machine learning; noninvasive system; noninvasive technique; pregnant woman; rapid analysis; squid array for reproductive assessment; unhealthy state; Accuracy; Data mining; Data models; Fetus; Heart beat; Heuristic algorithms; Pediatrics; Bioinformatics; Data mining; Decision trees; Machine learning;
Conference_Titel :
Information Security and Intelligence Control (ISIC), 2012 International Conference on
Conference_Location :
Yunlin
Print_ISBN :
978-1-4673-2587-5
DOI :
10.1109/ISIC.2012.6449776
