Preterm Birth Prediction of Pregnant Women in Post Conization Period Using Machine Learning Techniques

Code

• github.com/mahmudhasankhan/Preterm-Birth-Prediction
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Abstract

Pregnant women who underwent excisional surgeries (conization) for cervical intraepithelial neoplasia (CIN) display high risks of preterm birth. It is crucial to predict the risks of preterm birth amongst women in their post conization periods as this has severe consequences in terms of the cost as well as the health of the mother and the baby. This paper presents a preterm birth prediction system using machine learning approaches which will allow to evaluate the risk of a preterm birth. The dataset used in this work consisted of longitudinal cervical length (CL) of different gestational periods from 725 pregnant women undergoing surveillance programs in three clinics at London University Hospitals. Several machine learning algorithms were applied to make the prediction. Model based on decision tree achieved the highest accuracy (99.3%) on the test dataset.

Tasks

Reproductions