Statistic discipline, Khulna University, Khulna, 9208, Bangladesh.
BMC Womens Health. 2023 Oct 17;23(1):542. doi: 10.1186/s12905-023-02701-9.
Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.
针对妇女的家庭暴力在利比里亚普遍存在，近一半的妇女报告遭受过身体暴力。然而，关于导致这一问题的生物社会因素的研究仍然有限。本研究旨在利用 2019-2020 年利比里亚人口与健康调查（LDHS）的数据，采用机器学习方法预测妇女易遭受家庭暴力的情况。我们使用了七种机器学习算法来实现这一目标，包括 ANN、KNN、RF、DT、XGBoost、LightGBM 和 CatBoost。我们的分析表明，LightGBM 和 RF 模型在预测利比里亚妇女易遭受家庭暴力方面的准确率最高，分别为 81%和 82%。多个算法都确定了一个关键特征，即经历过情感暴力的人数。这些发现为了解与利比里亚针对妇女的家庭暴力相关的潜在特征和风险因素提供了重要的见解。通过利用机器学习技术，我们可以更好地预测和理解这一复杂问题，最终有助于制定更有效的预防和干预策略。
