Predictive Model Based on Machine Learning for the Detection of Physically Mistreated Women in the Peruvian Scope - Nemías Saboya; A. Angel Sullon; Omar L. Loaiza Jara (My University, 2019)
Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.
Keywords: Random forest; Classifier (UML); Recall; Decision tree; Machine learning; Artificial intelligence; Computer science; Decision tree learning; Precision and recall; Psychology; Cognitive psychology; Random forest; Classifier (UML); Recall; Decision tree; Machine learning; Artificial intelligence; Computer science; Decision tree learning; Precision and recall; Psychology; Social Sciences Social Sciences Health Intimate Partner and Family Violence; Social Sciences Social Sciences Gender Studies Sexual Assault and Victimization Studies; Social Sciences Social Sciences Sociology and Political Science Crime Patterns and Interventions