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dc.contributor.authorMaza Quiroga, Rosa María
dc.contributor.authorLópez-Rodríguez, Domingo 
dc.contributor.authorThurnhofer-Hemsi, Karl
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorJiménez Valverde, Clara
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2022-06-13T11:25:00Z
dc.date.available2022-06-13T11:25:00Z
dc.date.created2022-06-13
dc.date.issued2022-05
dc.identifier.urihttps://hdl.handle.net/10630/24354
dc.description.abstractThis paper explores the effect of using different pipelines to compute connectomes (matrices representing brain connections) and use them to train machine learning models with the goal of diagnosing Autism Spectrum Disorder. Five different pipelines are used to train six different ML models, splitting the data into female, male and all subsets so we can also research the effect of considering male and female patients separately. Our results conclude that pipeline and model choice impact results, along with using general or specific models.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Teches_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectAutismoes_ES
dc.subjectNeurociencia computacionales_ES
dc.subject.otherAutismes_ES
dc.subject.otherConnectomees_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherClassificationes_ES
dc.titleAnalysis of functional connectome pipelines for the diagnosis of autism spectrum disorderses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC)es_ES
dc.relation.eventplacePuerto de la Cruz, Tenerife, Españaes_ES
dc.relation.eventdate31/05/2022 - 03/06/2022es_ES


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