Department of Statistics, Jagannath University, Dhaka 1100, Bangladesh.
Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh.
J Healthc Eng. 2022 May 28;2022:1460908. doi: 10.1155/2022/1460908. eCollection 2022.
Intended pregnancy is one of the significant indicators of women's well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15-49 were eligible for this study. An independent test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen's Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30-49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.
有意妊娠是女性健康的重要指标之一。全球每年有 7400 万女性意外怀孕。在孟加拉国，已婚女性中 28%的妊娠属于意外妊娠。本研究旨在探讨六种不同机器学习（ML）算法在预测孟加拉国已婚女性意外妊娠中的应用效果。在考虑六种流行的 ML 算法（如逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）、k-最近邻（KNN）、朴素贝叶斯（NB）和弹性网络回归（ENR）来预测意外妊娠之前，我们先进行了独立测试。准确性、敏感度、特异度、科恩氏 Kappa 统计量和曲线下面积（AUC）值被用作模型评估。双变量分析结果表明，年龄在 30-49 岁、贫穷、未受教育和生活在男性主导家庭的妇女意外妊娠的比例较高。我们发现了各种用于意外妊娠分类的性能参数：LR 准确性=79.29%，LR AUC=72.12%；RF 准确性=77.81%，RF AUC=72.17%；SVM 准确性=76.92%，SVM AUC=70.90%；KNN 准确性=77.22%，KNN AUC=70.27%；NB 准确性=78%，NB AUC=73.06%；ENR 准确性=77.51%，ENR AUC=74.67%。根据 AUC 值，我们可以得出结论，在所研究的所有 ML 算法中，ENR 算法对预测孟加拉国妇女意外妊娠的分类最准确。我们的研究结果有助于更好地了解如何对孟加拉国妇女的妊娠意图进行分类。因此，政府可以发起一项有效的提高避孕意识的运动。
