image: professor Nan-Hee Kim(far left)
Credit: korea university medicine
Predicting Diabetes via Clinical and Genetic Data
Research Team Registered Patent for Diabetes Prediction System
- Predicting 5-year risk of type 2 diabetes with deep learning algorithms
-Anticipating benefits of personalized early diagnosis and prevention strategies
A research team led by professor Nan-Hee Kim from Korea University's College of Medicine (Nan-Hee Kim and So-Young Park of Department of Endocrinology and Metabolism; Min-Hee Kim and Jae-Young Kim of Health Science Research Center) recently developed a diabetes prediction system using clinical and genetic data. The system was registered for the domestic patent.
The diabetes prediction system is designed to predict the risk of diabetes utilizing clinical and genetic information through deep learning artificial intelligence algorithms.
The number of type 2 diabetes patients is rising globally, and it is caused by insulin secretion and insulin resistance due to genetic and environmental factors such as: aging, obesity, and lifestyle habits. Preventing the onset of diabetes is of the utmost importance because once the disease occurs, it is difficult to manage and can lead to various complications. If this innovative prediction system is commercialized in the medical field, it will assist healthcare professionals to identify individuals at high risk for developing diabetes and implement early preventive strategies.
When applying the diabetes prediction system, users' clinical and genetic information is transmitted and managed. Here, the information includes clinical data such as blood sugar levels, physical activity, and obesity, as well as genetic data that increases the risk of developing diabetes. Based on this information, the artificial intelligence algorithm calculates the likelihood of developing diabetes within the next five years, and users can receive a risk assessment report.
Professor Nan-Hee Kim said, "This diabetes prediction system can make precise predictions by reflecting the users' personal and genetic characteristics along with their lifestyle habits. This will allow us to predict the high risk for developing diabetes and take early preventive measures."