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Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

DC Field Value Language
dc.contributor.author김형우-
dc.date.accessioned2020-03-17T01:13:17Z-
dc.date.available2020-03-17T01:13:17Z-
dc.date.issued2020-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/175401-
dc.description.abstractCell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfScientific Reports-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleArtificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorShabbir Syed-Abdul-
dc.contributor.googleauthorRianda-Putra Firdani-
dc.contributor.googleauthorHee-Jung Chung-
dc.contributor.googleauthorMohy Uddin-
dc.contributor.googleauthorMina Hur-
dc.contributor.googleauthorJae Hyeon Park-
dc.contributor.googleauthorHyung Woo Kim-
dc.contributor.googleauthorAnton Gradiš다-
dc.contributor.googleauthorErik Dovgan-
dc.identifier.doi10.1038/s41598-020-61247-0-
dc.contributor.localIdA01151-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.contributor.alternativeNameKim, Hyung Woo-
dc.contributor.affiliatedAuthor김형우-
dc.citation.volume10-
dc.citation.startPage4583-
dc.identifier.bibliographicCitationScientific Reports, Vol.10 : 4583, 2020-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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