Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales

Hanna Christiansen, Mira Lynn Chavanon, Oliver Hirsch*, Martin H. Schmidt, Christian Meyer, Astrid Müller, Hans Juergen Rumpf, Ilya Grigorev, Alexander Hoffmann

*Corresponding author for this work

Abstract

A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of.80; precision (positive predictive value) ranged between.78 (gambling) and.92 (obesity), recall (sensitivity) between.58 for obesity and.87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.

Original languageEnglish
Article number18871
JournalScientific Reports
Volume10
Issue number1
Pages (from-to)18871
ISSN2045-2322
DOIs
Publication statusPublished - 02.11.2020

Research Areas and Centers

  • Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)

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