New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetesopen access
- Authors
- Tagmatova, Zarnigor; Abdusalomov, Akmalbek; Nasimov, Rashid; Nasimova, Nigorakhon; Dogru, Ali Hikmet; Cho, Young-Im
- Issue Date
- Sep-2023
- Publisher
- MDPI
- Keywords
- synthetic medical data; type 2 diabetes; prediction of diseases; shuffling
- Citation
- BIOENGINEERING-BASEL, v.10, no.9
- Journal Title
- BIOENGINEERING-BASEL
- Volume
- 10
- Number
- 9
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90259
- DOI
- 10.3390/bioengineering10091031
- ISSN
- 2306-5354
2306-5354
- 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.
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