IDENTIFICATION OF SIGNIFICANT FACTORS FOR BIRTH MODE IN BANGLADESH: EVIDENCE FROM CROSS-SECTIONAL STUDY

Abstract

Maternal mortality and difficulties during childbirth are important delivery issues in the majority of underdeveloped nations, including Bangladesh. Our objective is to figure out what's behind the rise in caesarean deliveries, build a link between critical components and caesarean section, and seek the key factors are related with caesarean births. We combined 2014 and 2017-18 BDHS data and found that key factors. We employed the logistic regression model to identify and quantify the effects of variables on birth mode and proposed five supervised machine learning approaches to find out the best performing model to predict the birth mode. The performance of these algorithms is evaluated by accuracy, precision, recall and F1 score. This study shows that Division, Highest education level, Wealth index, Total number of children born, BMI and Age of respondent at first birth, Husband’s education level and Respondent’s current working status have been statistically linked to caesarean delivery, and using the logistic regression approach all of the categories of the variables are also statistically significant (p value- < 0.05). Since the rich and the upper class are more likely to have a caesarean section than the poor and middle class, they need to be made aware of the disadvantages of this procedure. At the same time, they need to be encouraged to adopt a common approach. As a result, our work has a substantial influence and plays a role in developing a preventative strategy for caesarean situations, particularly in Bangladesh.