Abstract
A woman's satisfaction with childbirth may have immediate and long-term effects on her health as well as on the relationship with her newborn child. The mode of baby delivery is genuinely vital to a delivery patient and her infant child. It might be a crucial factor for ensuring the safety of both the mother and the child. During the baby delivery, decision-making within a short time becomes very challenging for the physician. Besides, humans may make wrong decisions selecting the appropriate delivery mode of childbirth. A wrong decision increases the mother's life risk and can also be harmful to the newborn baby's health. Computer-aided decision-making can be an excellent solution to this problem. Considering this scope, we have built a supervised machine learning-based decision-making model to predict the most suitable childbirth mode that will reduce this risk. This work has applied 32 supervised classifier algorithms and 11 training methods on the real childbirth dataset from the Tarail Upazilla Health complex, Kishorganj, Bangladesh. We have also analyzed the result and compared them using various statistical parameters to determine the best-performed model. The quadratic discriminant analysis has shown the highest accuracy of 0.979992 with the F1 score of 0.979962. Using this model to decide the appropriate labor mode may significantly reduce maternal and infant health risks.
Highlights
We want to make sure that the mother and the child are safe
We have proposed a computerized method of decisionmaking for selecting the appropriate mode of childbirth
We showed and analyzed various methodology of supervised classifiers based on a real dataset of childbirth to figure out the best model to predict the suitable mode of delivery
Summary
We want to make sure that the mother and the child are safe. For this safety, the method of baby delivery is very significant. When a child contains a low-risk pregnancy and is in the head-down position, and the patient is at least 37 weeks pregnant, gynecologists suggest attempting a vaginal birth In this case, a newborn baby usually gets essential gut bacteria from the mother. There are cases like twins, or the mother has diabetes, high blood weight, HIV, or active herpes, or the baby is not in a head-down position, which complies the patient to have a c-section delivery It increases the risk of asthma from the early childhood of the baby. "National Low Birth Weight Survey Bangladesh, 2015" has reported that Maternal mortality and cesarean delivery rates have doubled compared to regular deliveries [5].
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