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Machine learning based risk prediction for Parkinson's disease with nationwide health screening data

DC Field Value Language
dc.contributor.author김용욱-
dc.contributor.author신재용-
dc.date.accessioned2023-03-03T02:59:41Z-
dc.date.available2023-03-03T02:59:41Z-
dc.date.issued2022-11-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/192951-
dc.description.abstractAlthough many studies have been conducted on machine learning (ML) models for Parkinson's disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAlgorithms-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMachine Learning-
dc.subject.MESHMale-
dc.subject.MESHNeural Networks, Computer-
dc.subject.MESHParkinson Disease* / diagnosis-
dc.subject.MESHParkinson Disease* / epidemiology-
dc.subject.MESHROC Curve-
dc.titleMachine learning based risk prediction for Parkinson's disease with nationwide health screening data-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Rehabilitation Medicine (재활의학교실)-
dc.contributor.googleauthorYou Hyun Park-
dc.contributor.googleauthorJee Hyun Suh-
dc.contributor.googleauthorYong Wook Kim-
dc.contributor.googleauthorDae Ryong Kang-
dc.contributor.googleauthorJaeyong Shin-
dc.contributor.googleauthorSeung Nam Yang-
dc.contributor.googleauthorSeo Yeon Yoon-
dc.identifier.doi10.1038/s41598-022-24105-9-
dc.contributor.localIdA00750-
dc.contributor.localIdA02140-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid36376523-
dc.contributor.alternativeNameKim, Yong Wook-
dc.contributor.affiliatedAuthor김용욱-
dc.contributor.affiliatedAuthor신재용-
dc.citation.volume12-
dc.citation.number1-
dc.citation.startPage19499-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.12(1) : 19499, 2022-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Preventive Medicine (예방의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Rehabilitation Medicine (재활의학교실) > 1. Journal Papers

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