  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.

Machine Learning;Machine Learning