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      Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

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          Abstract

          In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

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              An introduction to ROC analysis

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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                17 November 2020
                November 2020
                : 10
                : 11
                : 958
                Affiliations
                Research Institute of Health Sciences (IUNICS-IdISBa), University of the Balearic Islands, 07120 Palma de Mallorca, Spain; charles.santana@ 123456gmail.com
                Author notes
                Article
                diagnostics-10-00958
                10.3390/diagnostics10110958
                7697204
                33212774
                c8a6abc7-6794-47a7-8635-7eac7670e772
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 November 2020
                : 13 November 2020
                Categories
                Article

                chronic pain,machine learning,classification,questionnaires,qst

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