New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tagmatova, Zarnigor | - |
dc.contributor.author | Abdusalomov, Akmalbek | - |
dc.contributor.author | Nasimov, Rashid | - |
dc.contributor.author | Nasimova, Nigorakhon | - |
dc.contributor.author | Dogru, Ali Hikmet | - |
dc.contributor.author | Cho, Young-Im | - |
dc.date.accessioned | 2024-01-31T13:00:19Z | - |
dc.date.available | 2024-01-31T13:00:19Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.issn | 2306-5354 | - |
dc.identifier.issn | 2306-5354 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90259 | - |
dc.description.abstract | The lack of medical databases is currently the main barrier to the development of artificial intelligence-based algorithms in medicine. This issue can be partially resolved by developing a reliable high-quality synthetic database. In this study, an easy and reliable method for developing a synthetic medical database based only on statistical data is proposed. This method changes the primary database developed based on statistical data using a special shuffle algorithm to achieve a satisfactory result and evaluates the resulting dataset using a neural network. Using the proposed method, a database was developed to predict the risk of developing type 2 diabetes 5 years in advance. This dataset consisted of data from 172,290 patients. The prediction accuracy reached 94.45% during neural network training of the dataset. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes | - |
dc.type | Article | - |
dc.identifier.wosid | 001127267900001 | - |
dc.identifier.doi | 10.3390/bioengineering10091031 | - |
dc.identifier.bibliographicCitation | BIOENGINEERING-BASEL, v.10, no.9 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85172244865 | - |
dc.citation.title | BIOENGINEERING-BASEL | - |
dc.citation.volume | 10 | - |
dc.citation.number | 9 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | synthetic medical data | - |
dc.subject.keywordAuthor | type 2 diabetes | - |
dc.subject.keywordAuthor | prediction of diseases | - |
dc.subject.keywordAuthor | shuffling | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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