The combination of clinical judgement and predictive risk models crucially assist social workers to segregate children at risk of maltreatment and decide when authorities should intervene. Predictive risk modelling to address this matter has been initiated by several governmental welfare authorities worldwide involving administrative data and machine learning algorithms. While previous studies have investigated risk factors relating to child maltreatment, several gaps remain as to understanding how such risk factors interact and whether predictive risk models perform differently for children with different features. By integrating Principal Component Analysis and K-Means clustering, this paper presents initial findings of our work on the identification of such features as well as their potential effect on current risk modelling frameworks. This approach allows examining existent, unidentified yet, clusters of New Zealand (NZ) children reported with care and protection concerns, as well as to analyse their inner structure, and evaluate the performance of prediction models trained cluster wise. We aim to discover the extent of clustering degree required as an early step in the development of predictive risk models for child maltreatment and so enhance the accuracy of such models intended for use by child protection authorities. The results from testing LASSO logistic regression models trained on identified clusters revealed no significant difference in their performance. The models, however, performed slightly better for two clusters including younger children. our results suggest that separate models might need to be developed for children of certain age to gain additional control over the error rates and to improve model accuracy. While results are promising, more evidence is needed to draw definitive conclusions, and further investigation is necessary.

通过整合主成分分析和K均值聚类，本文初步发现了在目前风险建模框架中对特定特征进行辨识以及这些特征对其潜在影响的研究成果，并评估了按簇训练预测模型的性能。研究结果表明可能需要为特定年龄段的儿童开发单独的模型，以提高模型准确性。然而，还需要更多证据来得出明确的结论，需要进行进一步的调查。

利用聚类方法改善新西兰儿童福利系统的预测风险建模