Domestic Violence Data Analysis for Machine Learning Use: A Systematic Mapping

• Alfeu Buriti Pereira Júnior CESAR
• Francisco Icaro do Nascimento Ribeiro CESAR

Abstract

There has been a strong interest in machine learning techniques in recent years to help with data analysis and knowledge extraction from various fields. Domestic violence is a social and human health problem, where the use of technology should be very beneficial to support various decision-making. This article is designed to contribute and give an overview of the current state of publications on the subject and what data is most relevant to use in this type of analysis. In the end, 38 publications were selected to be studied from a total of 6,235 in four internationally recognized digital scientific libraries

Keywords: Machine Learning, Domestic Violence, Systematic Mapping

References

Abbott, J. (1997). Injuries and illnesses of domestic violence. Annals of Emergency Medicine, 29(6), 781–785.

Amrit, C., Paauw, T., Aly, R., e Lavric, M. (2017). Identifying child abuse through text mining and machine learning. Expert Systems with Applications, 88, 402–418.

Berk, R. A., Sorenson, S. B., e Barnes, G. (2016). Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions. Journal of Empirical Legal Studies, 13(1), 94–115.

Bowen, E. (2011). An overview of partner violence risk assessment and the potential role of female victim risk appraisals. Aggression and Violent Behavior, 16(3).

Brignone, L., e Gomez, A. M. (2017). Double jeopardy: Predictors of elevated lethality risk among intimate partner violence victims seen in emergency departments. Preventive Medicine, 103, 20–25.

Clark, C. J., Ferguson, G., Shrestha, B., Shrestha, P. N., Oakes, J. M., Gupta, J., … Yount, K. M. (2018). Social norms and women’s risk of intimate partner violence in Nepal. Social Science and Medicine, 202, 162–169.

Connolly, C., Huzurbazar, S., e Routh-McGee, T. (2000). Multiple parties in domestic violence situations and arrest. Journal of Criminal Justice, 28(3), 181–188.

Cools, S., e Kotsadam, A. (2017). Resources and Intimate Partner Violence in Sub-Saharan Africa. World Development, 95, 211–230.

DUTTON, D. G., e KROPP, P. R. (2000). A review of domestic violence risk instruments. Trauma, Violence and Abuse, SAGE.

Gilchrist, G., Dennis, F., Radcliffe, P., Henderson, J., Howard, L. M., e Gadd, D. (2019). The interplay between substance use and intimate partner violence perpetration: A meta-ethnography. International Journal of Drug Policy, 65, 8–23.

Laeheem, K., e Boonprakarn, K. (2017). Factors predicting domestic violence among Thai Muslim married couples in Pattani province. Kasetsart J. Social Sciences.

López-Ossorio, J. J., González Álvarez, J. L., Buquerín Pascual, S., García, L. F., e Buela-Casal, G. (2017). Risk factors related to intimate partner violence police recidivism in Spain Juan. International Journal of Clinical and Health Psychology.

Matud, M. P. (2007).Dating Violence and Domestic Violence. Journal Adolescent Health

Petersen, K., Feldt, R., Mujtaba, S., e Mattsson, M. (2008). S. Mapping Studies in Software Engineering.

Pietri, M., e Bonnet, A. (2017). Analyses des représentations précoces et de la personnalité chez les victimes de violences conjugales. Revue Europeenne de Psychologie Appliquee, 67(4), 199–206.

Poelmans, J., Elzinga, P., Viaene, S., e Dedene, G. (2011). Formally analysing the concepts of domestic violence. Expert Systems with Applications, 38(4).

Raj, A., Silverman, J. G., Klugman, J., Saggurti, N., Donta, B., e Shakya, H. B. (2018). Longitudinal analysis of the impact of economic empowerment on risk for intimate partner violence among married women in rural Maharashtra, India. Social Science and Medicine, 196(August 2017), 197–203.

Roy Chowdhury, S., Bohara, A. K., e Horn, B. P. (2018). Balance of Power, Domestic Violence, and Health Injuries. World Development, 102, 18–29.

Sanz-Barbero, B., Linares, C., Vives-Cases, C., González, J. L., López-Ossorio, J. J., e Díaz, J. (2018). Heat wave and the risk of intimate partner violence. Science of the Total Environment, 644, 413–419.

Sorenson, S. B., e Spear, D. (2018). New data on intimate partner violence and intimate relationships: Implications for gun laws and federal data collection. P. Medicine.

Spencer, C. M., Stith, S. M., e Cafferky, B. (2019). Risk markers for physical intimate partner violence victimization: A meta-analysis. Aggression and Violent Behavior.

Van der Put, C. E., Gubbels, J., e Assink, M. (2019). Predicting domestic violence: A meta-analysis on the predictive validity of risk assessment tools. Aggression and Violent Behavior.

Wawrzyniak, Z. M., Borowik, G., Szczechla, E., Michalak, P., Pytlak, R., Cichosz, P., … Perkowski, E. (2018). Relationships between Crime and Everyday Factors.

Willie, T. C., Stockman, J. K., Perler, R., e Kershaw, T. S. (2018). Associations between intimate partner violence, violence-related policies, and HIV diagnosis rate among women in the United States. Annals of Epidemiology, 28(12), 881–885.

Wright, E. N., Hanlon, A., Lozano, A., e Teitelman, A. M. (2019). The impact of intimate partner violence, depressive symptoms, alcohol dependence, and perceived stress on 30-year cardiovascular disease risk among young adult women.