Identifying non‐adult attention‐deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system

Abstract

Abstract 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