A research study on the cervical cerclage to deal with cervical insufficiency using machine learning

The modern era is responsible for reducing the fertility rate is in women due to many evolving factors such as stress, work pressure, sedentary lifestyle. This research article is focusing on evaluating the efficacy of cerclage in pregnant women with dilated cervix using the sample comprises numerus records. The study is considering the history of patients with the emergency Cervical Cerclage and applies machine learning model to predict the output in terms of efficacy of cerclage in pregnant women. The study is based on the data available on public domain of China (The Data-Center of China Public Health Science). The dataset considered for study comprises cases of women aged between 30 and 42 years of age with 16-24 weeks of pregnancy. The cases are studied and evaluated to train the machine learning models (XGBoost, Decision Trees and Bagging) to classify the positive outcomes with respect to cervical dilatation after successful inhibition of uterine contraction where emergency cervical cerclage can be recommended. Many other cases are considered and inferences are drawn on the basis of computational analysis. For cervical dilatation and amniotic sac prolapse, emergency cervical cerclage in the early second trimester can prolong the gestational period and improve the prognosis of mothers and infants with a significant clinical application value as per the literature review presented in this research study. The proposed research study is attempting to devise machine learning based prediction models to predict whether the women is suffering from cervical insufficiency (CI) on the basis of biomarker variables present in the dataset. The model is trained, validated and tested on the new data for predicting the likelihood chances of cervical insufficiency in the women with 16-24 weeks pregnancy. If the CI problem is determined on time then the lives of women and babies can be saved with the aid of treatments like Cervical Cerclage, bed rest and other temporary surgeries. The second phase of this work also determines whether a patient with CI complexity during pregnancy can opt for Cervical Cerclage (CC) treatment on the basis of trained model on historical data. Hybrid machine learning model and booting algorithms are used in second phase of the research to predict the treatment of CC for cervical insufficiency. This research study is worthy of clinical reference by performing the review based study and by devising the machine learning prediction models. The study signifies the emergency cervical cerclage which is accomplished in women with complications in pregnancy for saving the lives of pregnant women. The emergency cervical cerclage can certainly reduce the chances of preterm delivery as per the study outcome and can also improve the survival of neonatal.