Abstract Number:
3618
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Fu-Wen Liang (1), Chiao-Yun Huang (2)
Institutions:
(1) Kaohsiung Medican University, N/A, (2) Kaohsiung Medical University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Predictive analytics has been used in children's service. Factors associated with child maltreatment may be multi-dimensional mixed. However, few studies examined children at risk from a socioecological framework. The study aims to develop a predictive model for early detecting children at high risk using diverse linked administrative datasets through machine learning methods. Administrative data from health, human services, police affairs, and education sectors between 2011 and 2018 were retrieved and integrated. A 1:10 matched case-control method with different predictive analytics techniques was used to build a prediction model. There were 4431 children who were reported with risk before their fifth birthday and 40837 controls between 2012 and 2018. In general, the risk model developed in this study had good performance with both precision and recall rate greater than 0.90. The identified risk factors associated with children at risk varied across age groups. The developed risk model can be a decision aid in real practice to help early detect children at high risk.
Keywords:
Machine learning|Predictive analytics|Child protection| | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Disease Prediction
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