## 2 Related Works
Indeed, specifically, ML algorithms have also been applied to the field of violence and crime, with some efforts to do so going back more than 30 years, e.g., analysis via linear regressions time series to reflect the number of arrests per day [19]. [PERSON] [20] studied the possibility of future recidivism in offenders, applying neural networks, and achieving good results. The insights offered by algorithms can be used to decide about parole in interpersonal violence situations [21]. For example, [PERSON] et al. [22] used Random Forest algorithms and concluded that approximately 20% of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. Their results also proposed an important ranking of risk factors for multiple assaults. In order to obtain a prediction accurate enough, it is necessary to start with an adequate database. Fortunately, the rise of Big Data has led to the collection of all kinds of data over the last few decades, in particular regarding how society develops in terms of wealth, social stability, employment, culture, etc. Although GBV itself has been included as a feature to forecast crime, as shown in the work of [PERSON] and [PERSON] [23] where police calls were forecasted, violence against women has also been the variable studied in relation with other crucial factors, like unemployment [24] and the increasing recidivism among unemployed suspects. However, other less direct circumstances have also become the subject of study, such as in the work of [PERSON], where the influence of seasonal factors on domestic violence incidence was analyzed [25]. It is important, therefore, to bear in mind the intersectional nature of GBV. Many aspects can have a hand in influencing the course of violence, including poverty or health status [26]. Thus, although social wealth and the family's economic situation are drawn upon as the main characteristics involved, the wide variety of additional factors that can be found make a multidisciplinary approach necessary, incorporating environment factors, education, safety and security, health, and also, correlatively, the interaction of professionals in each sector [27]. Many of these variables are already taken into account when planning policies related to violence management [28]. Some previous papers have focused on the specific field of forecasting GBV by applying ML In 2017, [PERSON] [29] tried to forecast domestic homicides and serious violence by using a database reflecting these situations in the county of Dorset (United Kingdom) and evaluating the police protocol. In doing so, he found that predicting deadly domestic violence dependent on insight from earlier police contacts did not seem possible at present, given the discovery that less than 50% of these cases had occurrences of earlier police contact and that, when contact occurred, the connections were evaluated by the protocol as not being of a high hazard in 89% of cases. [PERSON] and [PERSON] [30] reproduced the methods used by [PERSON] and found false-negative risk assessments in 67% of the deadly violence cases that had prior contact with the police but were not classified within the existing protocol as high risk. They proposed that possible alternative predictors regarding sex, health, and other descriptors could improve the performance of the prediction. But this related data needs to be collected over a long period in order to obtain knowledge. [PERSON] et al. [31] used the data provided by the Mexican Women's Institute, combined with other local organizations, and developed a mathematical deterministic model, which took into account variables like violence index, violence in childhood, the acceptance of machismo, and external factors, among others. By utilizing mathematical models, the authors showed their model's capability for diagnosing GBV risk in a certain couple. Although an interesting approach, its focus was on differential equations, so it does not explore the whole of society in a certain territory.