The study proposes on pregnant women (Garbh-ini) cohort, a multidimensional longitudinal dataset purposely designed to study preterm birth. The study will apply data-driven machine learning approaches to develop an accurate and clinically useful model to predict the risk of preterm births. It will use multiple models for classification, with better objective functions and misclassification penalties that will aid in a higher rate of accurate predictions, and resampling of the data to avert biases arising from class imbalance. The primary deliverable will be dynamic prediction models that can predict, at different periods of gestation, the PTB risk using the clinical, epidemiological and imaging data.
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