Prof. Anita Raja presents paper titled "Using Kernel Methods and Model Selection for Prediction of Preterm Birth", co-authored by Ilia Vovsha, Ansaf Salleb-Aouissi, Tom Koch, Alex Rybchuk,  Axinia Radeva, Ashwath Rajan,Yiwen Huang, Hatim Diab, Ashish Tomar and Ronald Wapner at the 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles CA.

Details about the proceedings can be found here.

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 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.