New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.
Zarnigor TagmatovaAkmalbek Bobomirzaevich AbdusalomovRashid NasimovNigorakhon NasimovaAli Hikmet DogruYoung-Im ChoPublished in: Bioengineering (Basel, Switzerland) (2023)
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.
Keyphrases
- neural network
- big data
- artificial intelligence
- type diabetes
- machine learning
- electronic health record
- healthcare
- deep learning
- adverse drug
- end stage renal disease
- cardiovascular disease
- chronic kidney disease
- ejection fraction
- glycemic control
- newly diagnosed
- emergency department
- peritoneal dialysis
- insulin resistance
- prognostic factors
- weight loss
- patient reported
- virtual reality