Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD)

Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD. In a four-year follow-up period, 1,680 children were later identified with ADHD using case definition. We trained and tested machine learning models to predict ADHD prospectively. The best-performing model using administrative and EDI data could reliably predict ADHD and achieved an Area Under the Curve (AUC) of 0.811 during cross-validation. Key predictive factors included EDI subdomain scores, sex, and socioeconomic status. Our findings suggest that machine learning algorithms that use population-level surveillance data could be a valuable tool for early identification of ADHD.Author summary: Many children exhibit symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) at a young age, but it is often diagnosed at a later stage. This delay in diagnosis can deprive children of the necessary support that they require. To address this issue, we conducted a study to develop a model that could predict ADHD in kindergarteners. We analyzed various information readily available for this age group in 2016, including health records, demographics, and teacher-rated developmental assessments. We then followed these children for four years to evaluate the accuracy of our model in predicting their later ADHD diagnosis. Our findings were promising, particularly when we used all the available data. The scores from developmental assessments were a significant factor in predicting the diagnosis accurately, along with other health and demographic factors. Our results suggest that machine learning could be an effective tool in helping parents, teachers, and doctors identify children with ADHD earlier, leading to better and more timely support.