ArticleBMC women's health2023
A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.
Article in BMC women's health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.Journal of clinical nursing · 2026Pooled it
- Artificial intelligence in intimate partner violence risk pathways: a PRISMA-ScR review of femicide prevention and medico-legal accountability.Frontiers in digital health · 2026Pooled it
- Artificial Intelligence in Addressing Interpersonal Violence Against Vulnerable Populations: A Scoping Review.Revista brasileira de enfermagem · 2026Article
- Determining domestic violence against women using machine learning methods: The case of Türkiye.Journal of evaluation in clinical practice · 2025Article
- Classification of domestic violence Persian textual content in social media based on topic modeling and ensemble learning.Heliyon · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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