Approved Research

Professor Huabing Zhang

Lay summary

Age-related diseases and cardiometabolic diseases such as diabetes mellitus, cardiovascular diseases and chronic kidney disease are among the leading causes of morbidity and mortality worldwide. Risk assessment provides clinicians with a tool to assess prevention and treatment options for patients at greatest risk of disease development and will help improve population health. Previous studies have reported some risk factors and prediction models for those diseases, but they are imperfect in their predictive abilities. Machine learning techniques can easily incorporate a large number of variables and their application in the medical field has yielded promising results. We aim to identify novel risk factors and generate novel prediction models for age-related diseases and cardiometabolic diseases using machine learning methods. Our study is devoted to identify high-risk groups in the population and find modifiable factors in order to reduce the disease burden. These results will also help clinicians to make better choices in diagnosis decisions, treatment decisions and prognosis managements for patients.