Prediction of Sexual Violence against Women (SVAW) Using Machine Learning
Abstract
Violence against women (VAW) constitutes a pressing issue that not only exacts a severe physical toll but also has significant psychological ramifications. The integration of technology with the healthcare sector, specifically the application of machine learning, presents a promising avenue for addressing these concerns. The objective of this study was to analyze potential factors contributing to the occurrence of VAW, especially sexual, and develop and assess a predictive process model. The research methodology employed in this study focuses on sexual violence against women (SVAW) and uses a quantitative approach. Data were collected from 600 married women through primary sources. The process model for predicting SVAW incorporates three algorithms: naive Bayes, random forest, and logistic regression. The most effective algorithm for predicting sexual violence against women was found to be random forest, with an accuracy of 90.00%. These findings offer valuable insights into the development of an enhanced process model for predicting SVAW. By harnessing the capabilities of machine learning techniques, we can gain a deeper understanding of this issue and ultimately contribute to the formulation of more targeted and effective prevention and intervention strategies tailored to the specific types of violence experienced by women.
Keywords: artificial intelligence, machine learning, Naive Bayes, random forest, logistic regression, violence against women, sexual violence.
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