Background: This research project aims to build a Machine Learning algorithm (ML) to predict first-time ADHD diagnosis, given that it is the most frequent mental disorder for the non-adult population.
Methods: We used a stacked model combining 4 ML approaches to predict the presence of ADHD. The dataset contains data from population health care administrative registers in Catalonia comprising 1,225,406 non-adult individuals for 2013-2017, linked to socioeconomic characteristics and dispensed drug consumption. We defined a measure of proper ADHD diagnoses based on medical factors.
Results: We obtained an AUC of 79.6% with the stacked model. Significant variables that explain the ADHD presence are the dispersion across patients' visits to healthcare providers; the number of visits, diagnoses related to other mental disorders and drug consumption; age, and sex.
Conclusions: ML techniques can help predict ADHD early diagnosis using administrative registers. We must continuously investigate the potential use of ADHD early detection strategies and intervention in the health system.
Keywords: attention‐deficit hyperactive disorder; comorbidity; machine learning.
© 2023 The Authors. JCPP Advances published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.