Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Kernel Methods and Model Selection for Prediction of Preterm Birth   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.

2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph