Machine Learning Algorithm-Based Contraceptive Practice among Ever-Married Women in Bangladesh: A Hierarchical Machine Learning Classification Approach

Abstract

Contraception enables women to exercise their human right to choose the number and spacing of their children. The present study identified the best model selection procedure and predicted contraceptive practice among women aged 15–49 years in the context of Bangladesh. The required information was collected through a well-known nationally representative secondary dataset, the Bangladesh Demographic and Health Survey (BDHS), 2014. To identify the best model, we applied a hierarchical logistic regression classifier in the machine learning process. Seven well-known ML algorithms, such as logistic regression (LR), random forest (RF), naïve Bayes (NB), least absolute shrinkage and selection operation (LASSO), classification trees (CT), AdaBoost, and neural network (NN) were applied to predict contraceptive practice. The validity computation findings showed that the highest accuracy of 79.34% was achieved by the NN method. According to the values obtained from the ROC, NN (AUC = 86.90%) is considered the best method for this study. Moreover, NN (Cohen’s kappa statistic = 0.5626) shows the most extreme discriminative ability. From our research, we suggest using the artificial neural network technique to predict contraceptive use among Bangladeshi women. Our results can help researchers when trying to predict contraceptive practice.