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dc.contributor.authorSimić, Svetlana
dc.contributor.authorVillar, José R.
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorSekulić, Slobodan R.
dc.contributor.authorSimić, Svetislav D.
dc.contributor.authorSimić, Dragan
dc.date.accessioned2021-03-29T11:33:26Z
dc.date.available2021-03-29T11:33:26Z
dc.date.issued2021
dc.identifier.citationSimić, S.; Villar, J.R.; Calvo-Rolle, J.L.; Sekulić, S.R.; Simić, S.D.; Simić, D. An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches. Int. J. Environ. Res. Public Health 2021, 18, 1890. https://doi.org/10.3390/ijerph18041890es_ES
dc.identifier.issn1661-7827
dc.identifier.urihttp://hdl.handle.net/2183/27630
dc.description.abstract[Abstract] (1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; MINECO-TIN2017-84804-Res_ES
dc.description.sponsorshipGobierno del Principado de Asturias; FCGRUPIN-IDI/2018/000226es_ES
dc.description.sponsorshipSerbia. Ministry of Education, Science and Technological Development; 451-03-68/2020-14/200156es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://doi.org/10.3390/ijerph18041890es_ES
dc.rightsCreative Commons License Attribution 4.0 (CC BY 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectIntelligent systemes_ES
dc.subjectHeadacheses_ES
dc.subjectAnalytical hierarchy processes_ES
dc.subjectFuzzy c-means clusteringes_ES
dc.titleAn application of a hybrid intelligent system for diagnosing primary headacheses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInternational Journal of Environmental Research and Public Healthes_ES
UDC.volume18es_ES
UDC.issue4es_ES
UDC.startPage1es_ES
UDC.endPage14es_ES
dc.identifier.doihttps://doi.org/10.3390/ijerph18041890


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