بررسی عوامل خطر تولد زودرس با استفاده از داده کاوی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22199||2011||4 صفحه PDF||سفارش دهید||2797 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 5, May 2011, Pages 5384–5387
Preterm birth is the leading cause of perinatal morbidity and mortality, but a precise mechanism is still unknown. Hence, the goal of this study is to explore the risk factors of preterm using data mining with neural network and decision tree C5.0. The original medical data were collected from a prospective pregnancy cohort by a professional research group in National Taiwan University. Using the nest case-control study design, a total of 910 mother–child dyads were recruited from 14,551 in the original data. Thousands of variables are examined in this data including basic characteristics, medical history, environment, and occupation factors of parents, and variables related to infants. The results indicate that multiple birth, hemorrhage during pregnancy, age, disease, previous preterm history, body weight before pregnancy and height of pregnant women, and paternal life style risk factors related to drinking and smoking are the important risk factors of preterm birth. Hence, the findings of our study will be useful for parents, medical staff, and public health workers in attempting to detect high risk pregnant women and provide intervention early to reduce and prevent preterm birth.
Preterm birth, the birth of an infant prior to 37 completed weeks of gestation, is the leading cause of perinatal morbidity and mortality (Goldenberg et al., 2008 and McCormick, 1985). The prevalence rate for such birth is about 12–13% in the USA, and 5–9% in Europe, other developed countries and Taiwan (Chuang et al., 2007, MacDorman et al., 2005 and Slattery and Morrison, 2002). The reasons for preterm birth remain unclear, although data mining is a promising approach to explore potential factors from large amount of data (Chang, 2007, Chen et al., 2009, Courtney et al., 2008 and Liao et al., 2008). Hence, the purpose of this work, based on the nest case-control study design, is to explore the risk factors of preterm by neural network and decision tree in data mining, to find more potential information.
نتیجه گیری انگلیسی
Preterm birth is the leading cause of perinatal morbidity and mortality, but to date the precise mechanism is still unknown now, and few prospective studies have been undertaken that explore the risk factors using data mining methods. Hence, we conducted a study based on the nest case-control design method and used data mining to explore risk factors of preterm. Our results show that multiple birth and hemorrhage during pregnancy are the top two risk factors. In addition, several maternal factors such as age, disease, previous preterm history, body weight before pregnancy and height, are also related to preterm birth. The prior statement about risks is commonly known and accepted at present. Furthermore, paternal risk factors related to, drinking, smoking, and occupation are also shown in our results. This is a significant suggestion that men also contribute to the risk of preterm birth. Thus, it is essential for prospective fathers to form good life style habits in order to prevent his baby from being born preterm. Hence, the findings of our study will be useful for parents, medical staff, and public health workers in attempting to detect high risk pregnant women and provide intervention early to reduce and prevent preterm birth.