利用核方法和模型选择预测早产
Using Kernel Methods and Model Selection for Prediction of Preterm Birth
摘要：
我们描述了机器学习在预测早产问题上的应用。我们对国家儿童健康和人类发展研究所（NICHD）收集的临床试验数据集进行了二次分析，同时将注意力集中在预测不同类别的早产上。我们比较了三种方法来获得预测模型：一个支持向量机（SVM）的方法与线性和非线性内核，逻辑回归与不同的模型选择以及模型的基础上，由医生专家规定的预测早产的决策规则。我们的方法突出了预处理方法，用于处理固有的动态，噪声和差距的数据和描述用于处理偏斜类分布的技术。经验性实验表明，与过去的工作相比，在预测早产方面有显着改善。
英文摘要：
We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.
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