<rdf:RDF xmlns:crm='http://www.cidoc-crm.org/rdfs/cidoc_crm_v5.0.2_english_label.rdfs#' xmlns:dc='http://purl.org/dc/elements/1.1/' xmlns:dcterms='http://purl.org/dc/terms/' xmlns:doap='http://usefulinc.com/ns/doap#' xmlns:edm='http://www.europeana.eu/schemas/edm/' xmlns:ekt='https://www.semantics.gr/authorities/schemanamespaces/ekt#' xmlns:foaf='http://xmlns.com/foaf/0.1/' xmlns:ore='http://www.openarchives.org/ore/terms/' xmlns:owl='http://www.w3.org/2002/07/owl#' xmlns:rdaGr2='http://rdvocab.info/ElementsGr2/' xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#' xmlns:skos='http://www.w3.org/2004/02/skos/core#' xmlns:svcs='http://rdfs.org/sioc/services#' xmlns:wgs84_pos='http://www.w3.org/2003/01/geo/wgs84_pos#' xmlns:xalan='http://xml.apache.org/xalan'><edm:ProvidedCHO rdf:about='https://www.openarchives.gr/aggregator-openarchives/edm/psepheda/000004-2159_29441'><dc:contributor xml:lang='el'>Χρήστου Βαρσακέλης, Δημήτριος</dc:contributor><dc:creator xml:lang='el'>Μπαλκούδη, Μιχαέλα</dc:creator><dc:description xml:lang='el'>Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2023.</dc:description><dc:description xml:lang='en'>Made available in DSpace on 2023-10-06T07:41:37Z (GMT). No. of bitstreams: 2
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  Previous issue date: 2023-08-29</dc:description><dc:description xml:lang='en'>Approved for entry into archive by Κυριακή Μπαλτά (balta@uom.gr) on 2023-10-06T07:41:37Z (GMT) No. of bitstreams: 2
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ΜπαλκούδηΜιχαέλαMSc2023.pdf.pdf: 1189968 bytes, checksum: 093e4efbd479c424e80b1c312cfaef82 (MD5)</dc:description><dc:description xml:lang='en'>In recent years, there has been an increase in awareness of mental health issues and it is widely
accepted that their early detection is essential to preventing social consequences. The use of
questionnaires is a common medical technique for promptly detecting mental health concerns.
Some scientists have proposed further automating the diagnosis of one mental condition by
utilizing a questionnaire that diagnoses another condition. However, more research and study
are required in order to prove the effectiveness of this further automation of the diagnosis of
mental disorders and make it practical. This thesis investigates two questions. First whether
a standardized memory questionnaire known as the PRMQ (Prospective and Recall Memory
Questionnaire) along with a few demographic and general health-related questions, may be
used to diagnose depression. Second, we try to investigate the reverse, that is whether memory-
related disorders may be diagnosed in patients by using a common questionnaire that makes
a diagnosis of depression called the ZUNG Depression Questionnaire (SDS), coupled with the
same demographic questions and health-related questions used in the first investigation. The
selection of these two mental illnesses is not arbitrary; rather, it is based on their usual co-
occurrence and the link that has been found between them. Both questions will be inves-
tigating via machine learning techniques. More specifically, question is approached in two
ways: as a regression and as a classification task. For each such task, suitable machine learn-
ing models are applied and compared in order to find the one with the best performance. The
memory-related classification task will turn out to be an imbalanced classification problem,
hence appropriate methods, such as resampling during training and cost-sensitive algorithms,
are used to resolve it. Our results show that we can diagnose depression through the memory
questionnaire, coupled with some demographic questions and health-related questions with an
accuracy of approximately 79%. The diagnosis of memory-related issues via the Zung depres-
sion questionnaire could not be achieved with adequate accuracy. This does not necessarily
imply that we can not diagnose memory-related issues from a depression questionnaire, but
more research is needed to improve performance.</dc:description><dc:identifier>http://dspace.lib.uom.gr/handle/2159/29441</dc:identifier><dc:publisher xml:lang='el'>Πανεπιστήμιο Μακεδονίας</dc:publisher><dc:rights xml:lang='el'>CC0 1.0 Παγκόσμια</dc:rights><dc:rights xml:lang='en'>http://creativecommons.org/publicdomain/zero/1.0/</dc:rights><dc:subject rdf:resource='http://semantics.gr/authorities/EKT-voc-classifier/605963148'></dc:subject><dc:subject xml:lang='en'>Memory issues</dc:subject><dc:subject xml:lang='en'>Prediction</dc:subject><dc:subject xml:lang='en'>Depression</dc:subject><dc:title xml:lang='en'>Machine learning-based mood classification via standardized questionnaires</dc:title><dc:type rdf:resource='http://semantics.gr/authorities/openarchives-item-types/metaptyxiakh-ergasia'></dc:type><dc:type xml:lang='en'>Electronic Thesis or Dissertation</dc:type><dc:type xml:lang='en'>Text</dc:type><dcterms:created>2023-10-06T07:41:37Z</dcterms:created></edm:ProvidedCHO><skos:Concept rdf:about='http://semantics.gr/authorities/EKT-voc-classifier/605963148'><skos:prefLabel xml:lang='el'>Τεχνητή νοημοσύνη</skos:prefLabel><skos:prefLabel xml:lang='en'>Artificial Intelligence</skos:prefLabel><skos:broader rdf:resource='http://semantics.gr/authorities/EKT-voc-classifier/1532468312'></skos:broader><skos:relatedMatch rdf:resource='http://id.loc.gov/authorities/subjects/sh85079324'></skos:relatedMatch><skos:relatedMatch rdf:resource='http://id.loc.gov/authorities/subjects/sh85031234'></skos:relatedMatch><skos:exactMatch rdf:resource='http://vocabularies.unesco.org/thesaurus/concept3052'></skos:exactMatch><skos:exactMatch rdf:resource='http://id.loc.gov/authorities/subjects/sh85008180'></skos:exactMatch><skos:exactMatch rdf:resource='http://semantics.gr/authorities/EKT-voc/605963148'></skos:exactMatch><skos:closeMatch rdf:resource='http://id.loc.gov/authorities/subjects/sh94004659'></skos:closeMatch><skos:note xml:lang='en'>isi - Computer Science, Artificial Intelligence covers resources that focus on research and techniques to create machines that attempt to efficiently reason, problem-solve, use knowledge representation, and perform analysis of contradictory or ambiguous information. This category includes resources on artificial intelligence technologies such as expert systems, fuzzy systems, natural language processing, speech recognition, pattern recognition, computer vision, decision-support systems, knowledge bases, and neural networks.</skos:note></skos:Concept><skos:Concept rdf:about='http://semantics.gr/authorities/openarchives-item-types/metaptyxiakh-ergasia'><skos:prefLabel xml:lang='el'>Μεταπτυχιακή εργασία</skos:prefLabel><skos:prefLabel xml:lang='en'>Master thesis</skos:prefLabel><skos:broader rdf:resource='http://semantics.gr/authorities/openarchives-item-types/Research-Paper-'></skos:broader><skos:exactMatch rdf:resource='http://vocab.getty.edu/aat/300077723'></skos:exactMatch></skos:Concept><ore:Aggregation rdf:about='https://www.openarchives.gr/aggregator-openarchives/edm/aggregation/provider/000004-2159_29441%231'><edm:aggregatedCHO rdf:resource='https://www.openarchives.gr/aggregator-openarchives/edm/psepheda/000004-2159_29441'></edm:aggregatedCHO><edm:dataProvider>Πανεπιστήμιο Μακεδονίας</edm:dataProvider><edm:isShownAt rdf:resource='https://dspace.lib.uom.gr/handle/2159/29441'></edm:isShownAt><edm:provider>Greek Aggregator OpenArchives.gr | National Documentation Centre (EKT)</edm:provider><edm:rights rdf:resource='http://creativecommons.org/publicdomain/zero/1.0/'></edm:rights></ore:Aggregation></rdf:RDF>