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

Date Published: April 25, 2019

Publisher: Public Library of Science

Author(s): Sarah Itani, Mandy Rossignol, Fabian Lecron, Philippe Fortemps, Ruxandra Stoean.


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.

Partial Text

Attention Deficit/Hyperactivity Disorder (ADHD) is a neuropsychiatric disorder which has an estimated overall prevalence of five to seven percent of youngsters [1]. Despite the neurocognitive origins of the syndrome, the clinical diagnosis of ADHD mainly relies on behavioral symptoms of inattention, hyperactivity and/or impulsivity, persisting for at least 6 months; such symptoms occuring before the age 12 and leading to the impairment of familial, social, or academic functioning [2]. More than ten years ago, it was claimed that the criteria established by the Diagnostic and Statistical Manual of Mental Disorders (DSM) are necessary but not sufficient for ADHD diagnosis [3]; there is still a need for more objective criteria on that regard. Yet, neuroimaging studies showed consistent structural and functional neural alterations related to ADHD [4, 5]. In order to provide objective observations, such alterations may be considered to complete the current assessment of the disorder and accordingly, to increase the agreement between clinicians, which is currently estimated at 61.0% [6].

In our work, we considered a data sample extracted from the open and freely available ADHD-200 collection [42]. We present the data in the first part of the section. Then, we explain the use of decision trees as predictive models. Finally, we reveal the analysis methodology of our study.

In this section, we give the results of our expert-based framework for the development of decision trees. As mentioned beforehand, we assess the predictive models against their explanatory power, i.e. the credibility of the decision chains. We give a summary of the results in the last part of the section.

In the sphere of translational neuroscience, studies based on machine learning approaches have been increasing over the last years. However, few of these studies have had a clinical impact as they have still not resulted in models that aid the diagnosis of disorders such as ADHD, whose physiological bases remain unknown.




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