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PREgDICT : Early prediction of gestational weight gain for pregnancy care
Journal Contribution - Journal Article Conference Contribution
Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error of 1.956 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.
Journal: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages: 4274 - 4278