Using Kernel Methods and Model Selection for Prediction of Preterm Birth
Abstract
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 Institute 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.
Cite
Text
Vovsha et al. "Using Kernel Methods and Model Selection for Prediction of Preterm Birth." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.Markdown
[Vovsha et al. "Using Kernel Methods and Model Selection for Prediction of Preterm Birth." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.](https://mlanthology.org/mlhc/2016/vovsha2016mlhc-using/)BibTeX
@inproceedings{vovsha2016mlhc-using,
title = {{Using Kernel Methods and Model Selection for Prediction of Preterm Birth}},
author = {Vovsha, Ilia and Salleb-Aouissi, Ansaf and Raja, Anita and Koch, Thomas and Rybchuk, Alex and Radeva, Axinia and Rajan, Ashwath and Huang, Yiwen and Diab, Hatim and Tomar, Ashish and Wapner, Ronald},
booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference},
year = {2016},
pages = {55-72},
volume = {56},
url = {https://mlanthology.org/mlhc/2016/vovsha2016mlhc-using/}
}