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dc.contributor.authorJeong Jae-Seung-
dc.contributor.author주현수-
dc.contributor.author조치현-
dc.date.accessioned2024-01-19T11:01:26Z-
dc.date.available2024-01-19T11:01:26Z-
dc.date.created2022-11-10-
dc.date.issued2022-10-
dc.identifier.issn1229-7771-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/114454-
dc.description.abstractArtificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.-
dc.languageKorean-
dc.publisher한국멀티미디어학회-
dc.title혈액암 인자 유효성 검증과 분류를 위한 진단 예측 알고리즘 성능 비교 분석-
dc.title.alternativeComparative Analysis of Diagnostic Prediction Algorithm Performance for Blood Cancer Factor Validation and Classification-
dc.typeArticle-
dc.identifier.doi10.9717/kmms.2022.25.10.1512-
dc.description.journalClass2-
dc.identifier.bibliographicCitation멀티미디어학회논문지, v.25, no.10, pp.1512 - 1523-
dc.citation.title멀티미디어학회논문지-
dc.citation.volume25-
dc.citation.number10-
dc.citation.startPage1512-
dc.citation.endPage1523-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.identifier.kciidART002892179-
dc.subject.keywordAuthorArtificial Intelligence-
dc.subject.keywordAuthorDigital Health Care-
dc.subject.keywordAuthorMachine Learning-
dc.subject.keywordAuthorDiagnostic Prediction Learning-
dc.subject.keywordAuthorClassifier-
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KIST Article > 2022
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