Tagmatova Zarnigor, Abdusalomov Akmalbek, Nasimov Rashid, Nasimova Nigorakhon, Dogru Ali Hikmet, Cho Young-Im
Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea.
Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan.
Bioengineering (Basel). 2023 Sep 1;10(9):1031. doi: 10.3390/bioengineering10091031.
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.
目前，医学数据库的缺乏是医学领域基于人工智能的算法发展的主要障碍。通过开发一个可靠的高质量合成数据库，这个问题可以得到部分解决。在本研究中，提出了一种仅基于统计数据开发合成医学数据库的简单可靠方法。该方法使用一种特殊的洗牌算法对基于统计数据开发的原始数据库进行更改，以获得满意的结果，并使用神经网络对所得数据集进行评估。使用所提出的方法，开发了一个数据库来提前5年预测2型糖尿病的发病风险。该数据集由172,290名患者的数据组成。在对该数据集进行神经网络训练期间，预测准确率达到了94.45%。
