Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

• Published in: PloS one Vol. 14; no. 4; p. e0215720
• Main Authors: Itani, Sarah, Rossignol, Mandy, Lecron, Fabian, Fortemps, Philippe
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.04.2019; Public Library of Science (PLoS)
• Subjects: Adolescent; Aircraft; Artificial intelligence; Attention Deficit Disorder with Hyperactivity - diagnosis; Attention Deficit Disorder with Hyperactivity - physiopathology; Attention deficit hyperactivity disorder; Brain research; Child; Child & adolescent psychiatry; Clinical decision making; Collection; Contests; Datasets as Topic; Decision analysis; Decision making; Decision Trees; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic and Statistical Manual of Mental Disorders; Diagnostic imaging; Engineering; Female; Gender differences; Humans; Hyperactivity; Impulsivity; Instructional materials; International conferences; Learning algorithms; Limbic system; Limbic System - physiopathology; Machine Learning; Male; Mathematical models; Medical diagnosis; Medical imaging; Medical research; Neurodevelopmental disorders; Neuroimaging; Neurology; Neurophysiology; Neurosciences; NMR; Nuclear magnetic resonance; Prognosis; Studies; Technology application; Belgium
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0215720

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.