Department of Health Informatics, School of Public Health, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia.
Department of Information Technology, College of Engineering, IT and Environment, Charles Darwin University, Darwin, Australia.
BMC Med Inform Decis Mak. 2023 Jan 17;23(1):9. doi: 10.1186/s12911-023-02102-w.
Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia.
Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software.
Random forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women's education level, family size, husband's desire for children, husband's education level, and women's fertility preference as predictors most frequently associated with contraceptive discontinuation.
Results have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.
在全球范围内，38%的避孕使用者在头十二个月内停止使用某种方法。在埃塞俄比亚，约 35%的避孕使用者也在十二个月内停止使用。停止使用会降低避孕覆盖率、计划生育方案的效果，并导致意外怀孕。因此，了解潜在的避孕方法停止使用的预测因素对于减少其不良后果至关重要。预测停止使用避孕药具的风险也可用作预警系统，通知计划生育方案。因此，本研究可以预测和确定埃塞俄比亚避孕方法停止使用的预测因素。
对 2016 年埃塞俄比亚人口与健康调查的二次数据进行了分析。总共对 5885 名妇女进行了 8 种机器学习算法的评估，并使用性能指标进行评估，以通过 python 软件预测和确定停止使用的重要预测因素。使用特征重要性方法选择停止使用避孕药具的最重要预测因素。最后，应用关联规则挖掘方法，通过 R 统计软件发现停止使用避孕药具与其最重要预测因素之间的关系。
随机森林是最具预测性的模型，准确率为 68%，确定了停止使用避孕药具的最重要预测因素。关联规则挖掘确定了妇女年龄、妇女教育程度、家庭规模、丈夫对孩子的愿望、丈夫教育程度和妇女生育意愿是与停止使用避孕药具最常相关的预测因素。
结果表明，机器学习算法可以准确预测避孕药具的停用状态，因此它们可能成为相关利益相关者的决策支持工具。通过对大型数据集的关联规则挖掘分析，我们的研究结果还揭示了以前未知的避孕方法停止使用和众多预测因素之间的模式和关系。
