儿童性虐待相关因素及严重性认知研究——以加勒区为例

L. Dilshan, N. Withanage, N. Chandrasekara
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摘要

儿童性虐待是一种全球性流行病,造成了毁灭性的后果。在世界上,四分之一的女孩和六分之一的男孩在年幼时曾遭受过某种形式的性虐待。根据警方统计,近年来斯里兰卡的儿童性虐待案件也在增加。加勒是报告的虐待儿童案件高发的四个地区之一,报告的CSA投诉也异常增加。此外,以前在该国南部地区没有进行过关于CSA危机的研究。因此,本研究的主要目的是确定影响加勒警察局CSA的关键风险因素,并开发合适的回归和机器学习模型来预测CSA的严重程度。本研究对2017年至2020年期间向加勒警察局儿童和妇女局报告的225起CSA案件进行了治疗。在从文献和领域专家的知识中发现的21个风险中,根据关联卡方检验,16个变量与CSA的反应变量严重程度呈显著关系。传统的OLR模型用于预测CSA的严重程度,并使用两种不同的数据选择方法检测CSA的关键风险因素。接下来,对机器学习技术:决策树、SVM和PNN进行训练,对CSA的严重程度进行分类。使用随机过采样技术来克服数据集中存在的类不平衡问题。最后,采用套袋技术来保持模型的健壮性并提高性能。OLR模型对CSA的严重程度进行了分类,准确率为68.85%。机器学习技术、决策树、SVM和PNN模型对CSA的严重程度进行了分类,准确率分别为82.15%、77.68%和85.25%。PNN模型比其他拟合模型具有更高的精度。这项研究的结果可用于采取预防措施,并为加勒警察局的成年人安排提高认识的会议,以减少CSA。此外,这项研究可以扩展到整个岛屿,以减少CSA,使其成为儿童更好的地方。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on Factors Associated with Child Sexual Abuse and Recognizing the Severity: Special Reference to Galle District
Child Sexual Abuse has been a global epidemic with devastating consequences. One in four girls and one in six boys have been experienced some form of sexual abuse in their tender age in the world. According to Police statistics, Child Sexual Abuse (CSA) cases is growing in recent years in Sri Lanka too. Galle is among the four districts where the reported child abuse cases high and the reported CSA complaints are increasing extraordinarily. Also, there is no previous research have been done in the Southern part of the country regarding the crisis of CSA. So, main objective of this study is to determine the key risk factors that affected to a CSA in Galle Police Division, and to develop suitable regression and machine learning models to predict the severity of CSA. 225 CSA cases reported to Police Child and Women Bureau of Galle Police Division during the period 2017 – 2020 were treated for this study. Out of twenty-one risk which were found from literature and knowledge of domain experts, sixteen variables showed a significant relationship with response variable severity of CSA according to chi-square test of association. Traditional OLR model was performed to predict severity of CSA and to detect key risk factors to a CSA with two different data selection methods. Next, machine learning techniques: Decision Tree, SVM, and PNN were trained to classify severity of CSA. Random over-sampling technique was used to overcome the class imbalanced problem persists in the dataset. Finally, bagging technique was executed to conserve robustness of models and to improve performance. The OLR model classified the severity of CSA with 68.85% accuracy. Machine learning techniques, Decision Tree, SVM and PNN model classified the severity of CSA with an accuracy of 82.15%, 77.68% and 85.25% respectively. PNN model performed with higher accuracy better than other fitted models. The results obtained from this study can be used to take precautions and to arrange awareness sessions for adults to reduce CSA in Galle Police Division. Also, the study can be extended to the whole island to reduce CSA and to make it a better place for children.
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