New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

Date
2023-09-01
Authors
Tagmatova, Zarnigor
Abdusalomov, Akmalbek
Nasimov, Rashid
Nasimova, Nigorakhon
Dogru, Ali Hikmet
Cho, Young-Im
Journal Title
Journal ISSN
Volume Title
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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.

Description
Keywords
synthetic medical data, type 2 diabetes, prediction of diseases, shuffling
Citation
Bioengineering 10 (9): 1031 (2023)
Department
Computer Science