Attention! Data Helps Diagnoses: A machine learning approach to predicting ADHD

Abstract

Attention deficit hyperactivity disorder (ADHD) is often dismissed as a “childhood condition”, since easy-to-identify features (e.g., hyperactivity) are more prevalent in children. Yet, for almost half of diagnosed individuals, the effects of ADHD persist through adulthood, impacting important areas such as jobs/academic performance and relationships. These implications make early diagnoses and effective treatments salient issues for medical professionals. However, as ADHD affects brain development, symptoms often greatly vary person to person. Further, research suggests that the high comorbidity of ADHD with other disorders compounds this issue, explaining why many diagnoses do not come until adulthood. One solution to more accurate diagnoses is machine learning, a class of models that have become increasingly prevalent in research. However, few researchers have developed models to predict ADHD diagnoses. In this study, we performed a secondary data analysis from a study on 103 anonymous participants (51 diagnosed with ADHD, 52 clinical controls). We employed a K-nearest neighbors algorithm to identify key features of ADHD (e.g., prevalence of comorbid disorders) that can accurately predict one’s diagnosis. The results of our analysis suggest: 1.) Objective metrics like this may improve ADHD diagnoses, since current methods are subjective and vary by physician, 2.) Some comorbidities are more predictive than others, and 3.) Research should continue in this area to include more predictive features. Implications for practitioners and researchers are discussed.