The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach
Groves, Laura Davies, Guy Oliver, Chris Allen, Debbie Bamford, Charlie Bell, Louise Brown, Chloe Cooper, Vivian Daniel, Louise Garstang, Jo
Groves, Laura
Davies, Guy
Oliver, Chris
Allen, Debbie
Bamford, Charlie
Bell, Louise
Brown, Chloe
Cooper, Vivian
Daniel, Louise
Garstang, Jo
Abstract
Background: Children with developmental disabilities show a high prevalence of behaviours that challenge (BtC). Thus, harnessing known risk markers to target early intervention to children at the greatest risk of BtC is essential. In this study, machine learning techniques were used to develop prediction models of risk (no, low and high severity behaviour) for different BtC (self-injurious behaviour, aggression, property destruction, 'any BtC'). A secondary aim was to assess the external validation of these models to predict future behaviour.
Method: Caregivers of individuals with developmental disabilities completed the Self-injury, Aggression and Destruction Screening Questionnaire. One dataset (n = 778) was used to train and test models to establish internal validation. Algorithms were created using random forest classifiers, K-nearest neighbours, multiple logistic regressions and Gaussian mixture models (GMM) for each type of behaviour. External validation utilising a second dataset of caregivers (n = 121) completing the SAD-SQ at baseline and 12 months later was then conducted.
Outcomes: Across internal and external validation, the random forest classifiers and GMM algorithms for any BtC showed the highest number of correct classifications with fair to good recall and precision, with 83.5% of people at risk of BtC correctly predicted. Predictions of persistence and incidence of behaviour over 12 months was also good (83.5% and 83.3%, respectively).
Interpretation: The novel prediction models showed the ability to predict BtC for children with developmental disabilities. Such models have applicability to clinical practice to inform provision of early preventative interventions for BtC.
MIDER Authors
Affiliations
University of Birmingham; Birmingham and Solihull, Mental Health NHS Foundation Trust; University of Wolverhampton; Wolverhampton and Sandwell CAMHS, Black Country Healthcare NHS Foundation Trust; Mersey Care NHS Foundation Trust; South West Yorkshire Partnership NHS Foundation Trust; The Challenging Behaviour Foundation, Chatham; University Hospitals Birmingham NHS Foundation Trust; Birmingham Community Healthcare NHS Foundation Trust; Saint Joseph's University, Philadelphia; Children's Hospital of Philadelphia; South London & Maudsley NHS Foundation Trust; Evelina London Children's Hospital; Coventry and Warwickshire Partnership Trust
Date
2026-07
Type
Article
Subject
Collections
Citation
Groves L, Davies G, Oliver C, Allen D, Bamford C, Bell L, Brown C, Cooper V, Daniel L, Garstang J, Jones C, McCleery JP, Liew A, Rose J, Simkiss D, Steenfeldt-Kristensen C, Welham A, Richards C. The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach. J Intellect Disabil Res. 2026 Jul;70(7):700-708. doi: 10.1111/jir.70105. Epub 2026 Apr 21. PMID: 42011951; PMCID: PMC13238332.