A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women
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 lear...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-10-01
|
| Series: | BMC Women's Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12905-023-02701-9 |
| _version_ | 1797451623293779968 |
|---|---|
| author | Riaz Rahman Md. Nafiul Alam Khan Sabiha Shirin Sara Md. Asikur Rahman Zahidul Islam Khan |
| author_facet | Riaz Rahman Md. Nafiul Alam Khan Sabiha Shirin Sara Md. Asikur Rahman Zahidul Islam Khan |
| author_sort | Riaz Rahman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T14:57:20Z |
| format | Article |
| id | doaj.art-9aa7323d35d5475f965c40b2acf8a020 |
| institution | Directory Open Access Journal |
| issn | 1472-6874 |
| language | English |
| last_indexed | 2024-03-09T14:57:20Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Women's Health |
| spelling | doaj.art-9aa7323d35d5475f965c40b2acf8a0202023-11-26T14:06:38ZengBMCBMC Women's Health1472-68742023-10-0123111510.1186/s12905-023-02701-9A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian womenRiaz Rahman0Md. Nafiul Alam Khan1Sabiha Shirin Sara2Md. Asikur Rahman3Zahidul Islam Khan4Statistic discipline, Khulna UniversityStatistic discipline, Khulna UniversityStatistic discipline, Khulna UniversityStatistic discipline, Khulna UniversityStatistic discipline, Khulna UniversityAbstract 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.https://doi.org/10.1186/s12905-023-02701-9XGBoostDecision treeK-NNCatBoostDomestic ViolenceMachine learning technique |
| spellingShingle | Riaz Rahman Md. Nafiul Alam Khan Sabiha Shirin Sara Md. Asikur Rahman Zahidul Islam Khan A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women BMC Women's Health XGBoost Decision tree K-NN CatBoost Domestic Violence Machine learning technique |
| title | A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women |
| title_full | A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women |
| title_fullStr | A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women |
| title_full_unstemmed | A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women |
| title_short | A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women |
| title_sort | comparative study of machine learning algorithms for predicting domestic violence vulnerability in liberian women |
| topic | XGBoost Decision tree K-NN CatBoost Domestic Violence Machine learning technique |
| url | https://doi.org/10.1186/s12905-023-02701-9 |
| work_keys_str_mv | AT riazrahman acomparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT mdnafiulalamkhan acomparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT sabihashirinsara acomparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT mdasikurrahman acomparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT zahidulislamkhan acomparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT riazrahman comparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT mdnafiulalamkhan comparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT sabihashirinsara comparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT mdasikurrahman comparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen AT zahidulislamkhan comparativestudyofmachinelearningalgorithmsforpredictingdomesticviolencevulnerabilityinliberianwomen |