Application of Machine Learning in predicting intimate partner-based violence among reproductive age women in Rwanda
Year: 2025
Type: preprint
Abstract: Background Intimate partner violence (IPV) is a serious global public health issue that predominantly affects women. Despite its widespread nature, IPV data and prevention strategies remain limited, particularly in low- and middle-income countries (LMICs), with sub-Saharan Africa bearing a significant burden. This study seeks to develop and validate a robust machine learning model to predict IPV and its predictors in Rwanda. Methods The study used five different machine learning algorithms, including the Least Absolute Shrinkage and Selection Operator (LASSO), logistic regression (LR), Random Forest, Support Vector Machine, and Classification and Regression Trees (CART) to develop models using data of 14,634 participants from the 2019–2020 Demographic and Health Survey. We used multiple imputation for missing data, and the Shapley Additive Explanations (SHAP) technique was used to select the most predictive variables. The data was randomly split into 80% and 20% for training and testing, respectively. To achieve a balanced dataset, the Synthetic Minority Over-sampling Technique was used on the training subset. The algorithms were evaluated using F1 score, AUC, accuracy, precision, sensitivity, and specificity. Results The random forest algorithm (accuracy and Receiver Operating Characteristic (ROC) curve of 99.1% and 100%, respectively) proved remarkably successful in accurately predicting IPV, outperforming other algorithms. Interestingly, the model’s performance revealed a significant improvement when utilizing only fifteen variables to predict IPV. The algorithm owes its success to its remarkable capability to identify crucial predictor features, with the top four being arguing with a partner, jealous partner, burning food, and insisting on knowing the respondent’s whereabouts. Conclusion The random forest algorithm was a highly effective tool for identifying potential predictors of IPV. Machine learning models may serve as an effective approach to address IPV and could help in early intervention and prevention by strengthening policy enforcement and integrating predictive tools into healthcare systems.
Source: Research Square
Language: English
Subfield: Health
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4416632923