A Resarch on the Factors Affecting the Outcomes of Child Abuse Cases Using Machine Learning Methods

Abstract

Modern information technology makes it possible to collect and store scientific and social research data. Some statistical methods can provide quite reliable results when the necessary assumptions are met in uncovering existing or hidden relationships between data. However, since data collected from real life often do not meet these assumptions, data mining methods that require fewer assumptions and can be applied to flexible and complex data sets have been developed for prediction. The use of machine learning methods, which include data mining techniques, to process data and produce meaningful information has become widespread in recent years. In this study, techniques such as the CHAID algorithm, an application of decision trees, and support vector machines, were compared with the logistic regression analysis method. The study’s sample consists of data from 61 child abuse cases in which the UCIM Saadet Öğretmen Association Struggling Child Abuse requested participation. The dependent variable of the study is whether the defendant received a sentence at the end of the trial, while the independent variables are five variables identified by leveraging expert (lawyer) opinions. As a result, it was found that the CHAID algorithm and support vector machines provided more accurate classification.

Keywords

• Machine Learning
• Logistic Regression
• Support Vector Machines
• CHAID Algorithm
• Child Abuse Cases.

References

1. Acuna E, Rodriguez C, (2004). The Treatment of Missing Values and Its Effect on Classifier Accuracy. In: Classification, Clustering, and Data Mining Applications, Springer, Berlin, Heidelberg, 639-647.
2. Agresti, A., (2019). An Introduction to Categorical Data Analysis, 3rd ed. Wiley pp 156-190.
3. Akin, M., Eyduran, E. & Reed, B.M. (2017). Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell Tiss Organ Cult 128, 303–316. https://doi.org/10.1007/s11240-016-1110-6
4. Allen-Collinson, J., (2009) A marked man: A case of female-perpetrated intimate partner abuse, International Journal of Men’s Health, 8 (1): 22-40.
5. Alp, S. & Öz, E.,(2019). Classification Methods and R Applications in Machine Learning, Nobel, Ankara, 140-175.
6. Aydav, P.S.S., Minz, S., (2020). Granulation-based self-training for the semi-supervised classification of remote-sensing images. Granul. Comput. 5, 309–327 https://doi.org/10.1007/s41066-019-00161-x
7. Bajpai, A. (2018). Child rights in India: Law, policy, and practice. Oxford University Press.
8. Bowlby, J. (1984). Violence in the family as a disorder of the attachment and caregiving systems. American journal of psychoanalysis, 44(1), 9.

9. Castro, F., Vellido, A., Nebot, À., Mugica, F. (2007). Applying Data Mining Techniques to e-Learning Problems. In:
10. Jain, L.C., Tedman, R.A., Tedman, D.K. (eds) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71974-8_8 pp 183-221
11. Chadaga, K., Prabhu, S., Sampathila, N. et al. (2024). An Explainable Framework to Predict Child Sexual Abuse Awareness in People Using Supervised Machine Learning Models. J. technol. behav. sci. 9, 346–362. https://doi.org/10.1007/s41347-023-00343-0
12. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS inc, 9, 13.
13. Chen, M.S., Han, J., Yu, P., (1996). "Data mining: an overview from a database perspective", IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-83.
14. Cheng, G., Chen, Q. & Zhang, R. (2021). Prediction of phosphorylation sites based on granular support vector machine. Granul. Comput. 6, 107–117. https://doi.org/10.1007/s41066-019-00202-5
15. Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. (2022). Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, 297(3), 1178-1192.
16. Fan, MH., Chen, MY. & Liao, EC., (2021). A deep learning approach for financial market prediction: utilization of Google trends and keywords. Granul. Comput. 6, 207–216. https://doi.org/10.1007/s41066-019-00181-7
17. Fouché, G., Langit, L. (2011). Introduction to Data Mining. In: Foundations of SQL Server 2008 R2 Business Intelligence. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-3325-1_14 pp 369-402
18. Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141-154.
19. Kass, G. (1980). An Exploratory Tecnique For İnvestigating Large Quantaties Of Categorical Data. Journal of the Royal Statistical Society., 29, 119-127.
20. Kassin, S. M., & Gudjonsson, G. H. (2004). The psychology of confessions: A review of the literature and issues. Psychological science in the public interest, 5(2), 33-67.
21. Kruttschnitt, C., Kalsbeek, W. D., & House, C. C. (Eds.). (2014). Estimating the incidence of rape and sexual assault (pp. 48109-1382). Washington, DC: National Academies Press.
22. Lippard ETC, Nemeroff CB. (2020 Jan). The Devastating Clinical Consequences of Child Abuse and Neglect: Increased Disease Vulnerability and Poor Treatment Response in Mood Disorders. Am J Psychiatry. 1;177(1):20-36.
23. Liu, H., Cocea, M. (2019). Granular computing-based approach of rule learning for binary classification. Granul. Comput. 4, 275–283. https://doi.org/10.1007/s41066-018-0097-2
24. Maharana, K., Mondal, S., Nemade, B., (2022). A review: Data pre-processing and data augmentation techniques, Global Transitions Proceedings, Volume 3, Issue 1, June 2022, Pages 91-99 https://doi.org/10.1016/j.gltp.2022.04.020
25. Mathews, B., & Collin-Vézina, D. (2019). Child Sexual Abuse: Toward a Conceptual Model and Definition. Trauma, Violence, & Abuse, 20(2), 131-148. https://doi.org/10.1177/1524838017738726
26. Nguyen-Thihong, D., Vo-Van, T. (2024). Classifying for interval and applying for image based on the extracted texture feature. Granul. Comput. 9, 29 https://doi.org/10.1007/s41066-024-00450-0
27. Noble, W. (2006). What is a support vector machine?. Nat Biotechnol 24, 1565–1567. https://doi.org/10.1038/nbt1206-1565
28. Paine, M. L., & Hansen, D. J. (2002). Factors influencing children to self-disclose sexual abuse. Clinical psychology review, 22(2), 271-295.
29. Pant, M., Kumar, S. (2022). Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method. Granul. Comput. 7, 861–879. https://doi.org/10.1007/s41066-021-00300-3.
30. Raschka, S., Patterson, J., Nolet, C. (2020). Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information , 11, 193. https://doi.org/10.3390/info11040193
31. Rybak, N., Hassall, M. (2022). Machine Learning–Enhanced Decision-Making. In: Hussain, C.M., Di Sia, P. (eds) Handbook of Smart Materials, Technologies, and Devices. Springer, Cham. https://doi.org/10.1007/978-3-030-84205-5_20
32. Tso, B. ve Mather P. M., (2009). Classification Methods For Remotely Sensed Data, Second Editon, Taylor & Francis Group, United States of America
33. Vapnik, V.(1963). Pattern recognition using generalized portrait method. Autom. Remote. Control. 24, 774–780 Walker-Descartes, I., Hopgood, G., Condado, L. V., & Legano, L. (2021). Sexual violence against children. Pediatric Clinics, 68(2), 427-436.
34. Wang, X., Ding, W., Liu, H. et al. (2020). Shape recognition through multi-level fusion of features and classifiers. Granul. Comput. 5, 437–448 https://doi.org/10.1007/s41066-019-00164-8 World Health Organization 2002. World Report On Violence And Health: Summary. Geneva
35. Yücesoy, E., Egrioglu, E. & Bas, E. (2023). A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis. Granul. Comput. 8, 1925–1935 https://doi.org/10.1007/s41066-023-00416-8.
36. Zalcberg, S. (2017). The place of culture and religion in patterns of disclosure and reporting sexual abuse of males: A case study of ultra orthodox male victims. Journal of Child Sexual Abuse, 26(5), 590-607.