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A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder

Publicado en:European Journal Of Neuroscience. 60 (3): 4115-4127 - 2024-02-20 60(3), DOI: 10.1111/ejn.16288

Autores: Caselles-Pina, Lucia; Quesada-Lopez, Alejandro; Sujar, Aaron; Hernandez, Eva Maria Garzon; Delgado-Gomez, David

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Resumen

Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder. The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases-Web of Science Core Collection, Scopus and Pubmed-with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis. Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder. This systematic review has highlighted the importance of attention deficit hyperactivity disorder diagnosis based on machine learning techniques as a complement to traditional assessment. Advances in machine learning will help make the diagnosis of attention deficit hyperactivity disorder much more simple and effective. Future machine learning studies in attention deficit hyperactivity disorder should improve the interpretability of their results to make them more accessible to health specialists. image

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