It is well known that there are some maternal and fetal issues that directly influence preterm births. However, all the variables provoking it are not completely determined. On the other hand, chronodisruption alters maternal circadian rhythms, with negative consequences for the maturation of the fetus. Thus, the objective of this work is to add other factors related to maternal chronodisruption factors and to check if all together can improve preterm birth prevention. The methodology followed to reach this objective is based on machine learning approach. The data are composed by a cohort of 380 births labelled as preterm or term births. Variables defining each individual are related to maternal habits, night exposure to light or sleep duration during gestation. In addition, maternal variables related to the gestation were obtained as well as fetal characteristics. Preliminary statistical tests confirm that cervix dilatation, fetus estimated weight and weight at birth were significantly lower (\(p<0.05\)) in preterm group than in term group as expected. A deeper study based on machine learning highlights some interesting and non obvious relations between some factors related to night exposure to light and sleeping habits. In fact, the decision tree obtained as predictive model indicates that light coming in through the window or lightness level of the bedroom during the night are key features in predicting preterm delivery.

众所周知，有些母婴问题直接影响早产。但是，尚未完全确定引起它的所有变量。另一方面，时膜破裂改变了母亲的昼夜节律，对胎儿的成熟有负面影响。因此，这项工作的目的是增加其他与产妇计时异常有关的因素，并检查所有这些因素是否可以改善早产预防。实现此目标所遵循的方法是基于机器学习方法的。数据由380个标记为早产或足月出生的婴儿组成。定义每个人的变量与母体习惯，夜间光照或妊娠期睡眠时间有关。此外，获得了与妊娠有关的母亲变量以及胎儿特征。初步统计测试证实子宫颈扩张，胎儿估计体重和出生时体重显着降低（早产组中的\（p <0.05 \））高于预期组。一项基于机器学习的更深入研究突显了与夜间光线照射和睡眠习惯相关的一些因素之间有趣且不明显的关系。实际上，作为预测模型获得的决策树表明，夜间通过窗户进入的光线或卧室的亮度水平是预测早产的关键特征。