A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.
BMC Womens Health
; 23(1): 542, 2023 10 17.
Article
in En
| MEDLINE
| ID: mdl-37848839
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Domestic Violence
Limits:
Female
/
Humans
Country/Region as subject:
Africa
Language:
En
Journal:
BMC Womens Health
Journal subject:
SAUDE DA MULHER
Year:
2023
Type:
Article
Affiliation country:
Bangladesh