"Proposal of a Model for the Classification of Severity of Family Violence Using Machine Learning Techniques"

Abstract

"Domestic violence is a social and public health problem with serious consequences for victims, as it causes physical, emotional, and psychological harm, and often leads to long-term effects that impact family dynamics and mental health. The objective of this study was to implement a predictive model for classifying the severity of domestic violence cases in Peru using Machine Learning techniques. The methodology was based on five phases: data acquisition from the NotiWeb system of the Ministry of Health (CDC-MINSA), preprocessing (data cleaning, transformation, and standardization), class balancing using SMOTE, implementation of eight supervised Machine Learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors, Support Vector Machine, Bagging, and AdaBoost), and model evaluation using five performance metrics. Multiple models achieved optimal performance, with Logistic Regression (LR), Random Forest (RF), and AdaBoost (ABC) consistently showing the best results across both training/test splits. LR demonstrated the most stable performance with Accuracy, Precision, Recall, F1-Score, and AUC-ROC of 0.9540, 0.9669, 0.9396, 0.9530, and 0.97, respectively. These results demonstrate the potential of predictive algorithms for early identification and prioritization of high-severity domestic violence cases, contributing to more effective institutional responses and public policy design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026."