PDF(Pubmed)
Abstract：
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.
摘要：
针对妇女的家庭暴力在利比里亚很普遍，近一半的女性报告身体暴力。然而,对促成这一问题的生物社会因素的研究仍然有限。本研究旨在利用机器学习方法预测女性遭受家庭暴力的脆弱性。利用2019-2020年利比里亚人口与健康调查(LDHS)的数据。我们采用了七种机器学习算法来实现这一目标，包括ANN,KNN,射频,DT,XGBoost,LightGBM,和CatBoost。我们的分析显示，LightGBM和RF模型在预测利比里亚妇女对家庭暴力的脆弱性方面取得了最高的准确性，准确率为81%和82%，分别。在多种算法中确定的关键特征之一是经历过情感暴力的人数。这些发现为利比里亚与针对妇女的家庭暴力有关的基本特征和风险因素提供了重要见解。通过利用机器学习技术，我们可以更好地预测和理解这个复杂的问题，最终有助于制定更有效的预防和干预策略。
