A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women

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 appr...

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Bibliographic Details
Published in:BMC women's health 2023-10, Vol.23 (1), p.1-542, Article 542
Main Authors: Rahman, Riaz, Khan, Md. Nafiul Alam, Sara, Sabiha Shirin, Rahman, Md. Asikur, Khan, Zahidul Islam
Format: Article
Language:English
Subjects:
Abused women
Accuracy
Algorithms
CatBoost
Comparative analysis
Comparative studies
Computational linguistics
Crimes against
Data mining
Datasets
Decision tree
Domestic Violence
Emotional abuse
Family violence
Gender
Influencer marketing
K-NN
Language processing
Machine learning
Machine learning technique
Marriage
Mental depression
Mental health
Natural language interfaces
Post traumatic stress disorder
Privacy
Rape
Risk factors
Sex crimes
Surveys
Unemployment
Violence
Violence against women
Vulnerability
Womens health
XGBoost
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Summary: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.
ISSN:1472-6874
1472-6874
DOI:10.1186/s12905-023-02701-9