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

Saime Şule Aksakal; Erol Eğrioğlu

Article Details

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

Authors:

Saime Şule Aksakal | Erol Eğrioğlu

Publisher:

Giresun University

Main Topics:

Machine Learning (Other) | Statistical Analysis | Statistical Data Science

Journal:

Turkish Journal of Forecasting Vol.9 Issue 1, pp.17-29

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001337724

pISSN:

2618-6594

Keywords:

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 studys 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.

 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 studys 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.

 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 studys 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.

 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 studys 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.

Article Details

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

Authors:

Saime Şule Aksakal | Erol Eğrioğlu

Publisher:

Giresun University

Main Topics:

Machine Learning (Other) | Statistical Analysis | Statistical Data Science

Journal:

Turkish Journal of Forecasting Vol.9 Issue 1, pp.17-29

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001337724

pISSN:

2618-6594

Machine Learning

Logistic Regression

Support Vector Machines

CHAID Algorithm

Child Abuse Cases.

Issues

Article Details

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

Authors: Saime Şule Aksakal | Erol Eğrioğlu

Publisher: Giresun University

Main Topics: Machine Learning (Other) | Statistical Analysis | Statistical Data Science

Journal: Turkish Journal of Forecasting Vol.9 Issue 1, pp.17-29

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001337724

pISSN: 2618-6594

Machine Learning

Logistic Regression

Support Vector Machines

CHAID Algorithm

Child Abuse Cases.

Issues

Authors:

Saime Şule Aksakal | Erol Eğrioğlu

Publisher:

Giresun University

Main Topics:

Machine Learning (Other) | Statistical Analysis | Statistical Data Science

Journal:

Turkish Journal of Forecasting Vol.9 Issue 1, pp.17-29

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001337724

pISSN:

2618-6594