A Study of Applying Artificial Intelligence to Premature

• Main Authors: Hom-Ting Lee, 李虹葶
• Other Authors: Chun-Lang Chang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/jpb7uh

碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 96 === According to the data of Ministry of Interior, there are approximately 200,000 neonates in Taiwan per year, but the death number of neonates is about 3097 to 4130. The happening rate of premature is 5~10% among the pregnant, but among death of neonates, the happening rate of premature accounts for 80%. Every kind of acute or chronic diseases accompanying with premature are hard to handle for those premature families. A premature infant needs to consume more medical resource than a normal neonate such as intensive care unit or incubator. Hence, how to utilize available knowledge in order to lower down the premature rate is considerable. This research applies artificial intelligence to establishing model of medical diagnose in order to offer physician to do omnibus diagnosis. We analyze the data of repeated antenatal examinations to predict whether the premature happens or not so as to provide a good environment to stabilize fetal position. Furthermore, we deliberate a positive and integrity auxiliary diagnosing system in order that the physician, expectant mother, and premature infant could have closer information communication, therapy and birth plan to decrease death rate of premature infant. This study applies decision tree and neural network to explore premature phenomenon. The research indicates that in the field of premature phenomenon, back-propagation N.N analysis has better predictive effect, and the testing result is 93.34%. Besides, in the field of being hospitalized, according to decision tree C5.0, the direct classifying rate 93.44% is the best. We can use it to establish diagnosing system in order to find out the main factor before parturition and then to undertake prevention and treatment.